Deep learning in video multi-object tracking: A survey
Open accessThe claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Multi-object tracking has been extensively explored in recent literature for a wide range of applications.
“Multi-object tracking (MOT) has been extensively explored in recent literature for a wide range of applications [86, 138].”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [86]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · see citing works
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803 · see citing works
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565 · see citing works
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · see citing works
A New Approach to Linear Filtering and Prediction Problems
Kalman, R. E. (1960) · Journal of Basic Engineering · doi:10.1115/1.3662552 · Cited by 31,751 · see citing works
(2016) · Cited by 31,590 · see citing works
He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.322 · Cited by 29,137 · see citing works
YOLO9000: Better, Faster, Stronger
Redmon, J., & Farhadi, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.690 · Cited by 19,892 · see citing works
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · doi:10.1109/cvpr.1993 · Cited by 17,989 · see citing works
YOLOv3: An Incremental Improvement
Redmon, J., & Farhadi, A. (2018) · arXiv · doi:10.48550/arxiv.1804.02767 · Cited by 17,141 · see citing works
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897 · see citing works
LIII. On lines and planes of closest fit to systems of points in space
Karl Pearson, F. R. S. (1901) · The London Edinburgh and Dublin Philosophical Magazine and Journal of Science · doi:10.1080/14786440109462720 · Cited by 13,658 · see citing works
The Hungarian method for the assignment problem
Kuhn, H. W. (1955) · Naval Research Logistics Quarterly · doi:10.1002/nav.3800020109 · Cited by 13,120 · see citing works
Proceedings of IEEE International Conference on Computer Vision
(1995) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466933 · Cited by 13,117 · see citing works
(1981) · Cited by 11,963 · see citing works
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
Arulampalam, M. S., Maskell, S. R., Gordon, N., & Clapp, T. C. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/78.978374 · Cited by 11,531 · see citing works
Vision meets robotics: The KITTI dataset
Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913491297 · Cited by 9,913 · see citing works
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853 · see citing works
Matching pursuits with time-frequency dictionaries
Mallat, S., & Zhang, Z. (1993) · IEEE Transactions on Signal Processing · doi:10.1109/78.258082 · Cited by 9,023 · see citing works
Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields
Cao, Z., Simon, T., Wei, S.-E., & Sheikh, Y. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.143 · Cited by 7,539 · cited 3× · see citing works
“Such CNN used the maps extracted by PafNet, and was trained using reinforcement learning.”
MethodOn the Properties of Neural Machine Translation: Encoder–Decoder Approaches
Cho, K., van Merriënboer, B., Bahdanau, D., & Bengio, Y. (2014) · SSST@EMNLP · doi:10.3115/v1/w14-4012 · Cited by 7,251 · see citing works
Zagoruyko, S., & Komodakis, N. (2016) · Procedings of the British Machine Vision Conference 2016 · doi:10.5244/c.30.87 · Cited by 7,244 · see citing works
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., & Keutzer, K. (2016) · arXiv · doi:10.48550/arxiv.1602.07360 · Cited by 6,201 · see citing works
High-Speed Tracking with Kernelized Correlation Filters
Henriques, J. F., Caseiro, R., Martins, P., & Batista, J. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2345390 · Cited by 5,753 · see citing works
Watkins, C. (1989) · OpenGrey (Institut de l'Information Scientifique et Technique) · doi:10.17863/cam.132164 · Cited by 5,406 · see citing works
Simple online and realtime tracking with a deep association metric
Wojke, N., Bewley, A., & Paulus, D. (2017) · 2017 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip.2017.8296962 · Cited by 5,286 · see citing works
Proceedings European Conference on Computer Vision 2002
Heyden, A., Sparr, G., Nielsen, M., & Johansen, P. (2002) · Lund University Publications (Lund University) · Cited by 5,153 · see citing works
R-FCN: Object Detection via Region-based Fully Convolutional NetworksPreprint
Dai, J., Li, Y., He, K., & Sun, J. Z. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.06409 · Cited by 5,135 · see citing works
An introduction to hidden Markov models
Rabiner, L., & Juang, B. (1986) · IEEE ASSP Magazine · doi:10.1109/massp.1986.1165342 · Cited by 4,775 · see citing works
Scalable Person Re-identification: A Benchmark
Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., & Tian, Q. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.133 · Cited by 4,728 · see citing works
Simple online and realtime tracking
Bewley, A., Ge, Z., Ott, L., Ramos, F., & Upcroft, B. (2016) · 2016 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip.2016.7533003 · Cited by 4,433 · see citing works
In Defense of the Triplet Loss for Person Re-IdentificationPreprint
Hermans, A., Beyer, L., & Leibe, B. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.07737 · Cited by 3,271 · see citing works
Performance Measures and a Data Set for Multi-target, Multi-camera Tracking
Ristani, E., Solera, F., Zou, R., Cucchiara, R., & Tomasi, C. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48881-3_2 · Cited by 3,104 · see citing works
An algorithm for quadratic programming
Frank, M., & Wolfe, P. (1956) · Naval Research Logistics Quarterly · doi:10.1002/nav.3800030109 · Cited by 3,030 · see citing works
Two-Frame Motion Estimation Based on Polynomial Expansion
Farnebäck, G. (2003) · Lecture notes in computer science · doi:10.1007/3-540-45103-x_50 · Cited by 3,010 · see citing works
Long short-term memory recurrent neural network architectures for large scale acoustic modeling
Sak, H., Senior, A., & Beaufays, F. (2014) · Interspeech 2014 · doi:10.21437/interspeech.2014-80 · Cited by 2,973 · see citing works
Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics
Bernardin, K., & Stiefelhagen, R. (2008) · EURASIP Journal on Image and Video Processing · doi:10.1155/2008/246309 · Cited by 2,918 · see citing works
Region-based convolutional networks for accurate object detection and segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2437384 · Cited by 2,912 · see citing works
Learning Multi-domain Convolutional Neural Networks for Visual Tracking
Nam, H., & Han, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.465 · Cited by 2,806 · see citing works
ECO: Efficient Convolution Operators for Tracking
Danelljan, M., Bhat, G., Khan, F. S., & Felsberg, M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.733 · Cited by 2,805 · see citing works
DeepReID: Deep Filter Pairing Neural Network for Person Re-identification
Li, W., Zhao, R., Xiao, T., & Wang, X. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.27 · Cited by 2,705 · see citing works
SIGNATURE VERIFICATION USING A “SIAMESE” TIME DELAY NEURAL NETWORK
BROMLEY, J., BENTZ, J. W., BOTTOU, L., GUYON, I., LECUN, Y., MOORE, C., SÄCKINGER, E., & SHAH, R. (1994) · Series in Machine Perception and Artificial Intelligence · doi:10.1142/9789812797926_0003 · Cited by 2,350 · see citing works
Livescu, K., Bezman, A., Borges, N., Yung, L.-Y., Çetin, Ö., Frankel, J., King, S., Magimai.-Doss, M., Chi, X., & Lavoie, L. (2007) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 2,145 · see citing works
Signature Verification Using A "Siamese" Time Delay Neural Network
BROMLEY, J., BENTZ, J. W., BOTTOU, L., GUYON, I., LECUN, Y., MOORE, C., SÄCKINGER, E., & SHAH, R. (1993) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001493000339 · Cited by 2,077 · see citing works
RMPE: Regional Multi-person Pose Estimation
Fang, H.-S., Xie, S., Tai, Y., & Lu, C. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.256 · Cited by 1,976 · see citing works
LSTM neural networks for language modeling
Sundermeyer, M., Schlüter, R., & Ney, H. (2012) · Interspeech 2012 · doi:10.21437/interspeech.2012-65 · Cited by 1,963 · see citing works
The Gaussian Mixture Probability Hypothesis Density Filter
Vo, B., & Ma, W. (2006) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2006.881190 · Cited by 1,946 · see citing works
Fast Feature Pyramids for Object Detection
Dollár, P., Appel, R., Belongie, S., & Perona, P. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2300479 · Cited by 1,941 · see citing works
ViBe: A Universal Background Subtraction Algorithm for Video Sequences
Barnich, O., & Droogenbroeck, M. V. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2101613 · Cited by 1,893 · see citing works
Wide Residual NetworksPreprint
Zagoruyko, S., & Komodakis, N. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.07146 · Cited by 1,850 · see citing works
Hierarchical Convolutional Features for Visual Tracking
Ma, C., Huang, J., Yang, X., & Yang, M. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.352 · Cited by 1,811 · see citing works
Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features
Gray, D., & Hai, T. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88682-2_21 · Cited by 1,511 · see citing works
Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
Bell, S., Zitnick, C. L., Bala, K., & Girshick, R. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.314 · Cited by 1,344 · see citing works
Pedestrian detection: A benchmark
Dollár, P., Wojek, C., Schiele, B., & Perona, P. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206631 · Cited by 1,335 · see citing works
MOT16: A Benchmark for Multi-Object Tracking
Milan, A., Leal-Taixe, L., Reid, I., Roth, S., & Schindler, K. (2016) · arXiv · doi:10.48550/arxiv.1603.00831 · Cited by 1,309 · see citing works
Instance-aware Semantic Segmentation via Multi-task Network Cascades
Dai, J., He, K., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.04412 · Cited by 1,273 · see citing works
Instance-Aware Semantic Segmentation via Multi-task Network Cascades
Dai, J., He, K., & Sun, J. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.343 · Cited by 1,251 · see citing works
On the Properties of Neural Machine Translation: Encoder-Decoder ApproachesPreprint
Cho, K., van Merriënboer, B., Bahdanau, D., et al. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.1259 · Cited by 1,112 · see citing works
Multiple object tracking: A literature review
Luo, W., Xing, J., Milan, A., Zhang, X.-Q., Liu, W., Zhao, X., & Kim, T.-K. (2014) · Artificial Intelligence · doi:10.1016/j.artint.2020.103448 · Cited by 1,095 · see citing works
DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation
Pishchulin, L., Insafutdinov, E., Tang, S., Andres, B., Andriluka, M., Gehler, P., & Schiele, B. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.533 · Cited by 1,090 · see citing works
DeepFlow: Large Displacement Optical Flow with Deep Matching
Weinzaepfel, P., Revaud, J., Harchaoui, Z., & Schmid, C. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.175 · Cited by 1,019 · see citing works
Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification
Xiao, T., Li, H., Ouyang, W., & Wang, X. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.140 · Cited by 971 · see citing works
Learning Social Etiquette: Human Trajectory Understanding In Crowded Scenes
Robicquet, A., Sadeghian, A., Alahi, A., & Savarese, S. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46484-8_33 · Cited by 927 · see citing works
MOTChallenge 2015: Towards a Benchmark for Multi-Target TrackingPreprint
Leal-Taixé, L., Milan, A., Reid, I. D., Roth, S., & Schindler, K. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.01942 · Cited by 923 · see citing works
Person Re-identification in the Wild
Zheng, L., Zhang, H., Sun, S., Chandraker, M. K., Yang, Y., & Tian, Q. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.357 · Cited by 887 · see citing works
Deeply-Learned Part-Aligned Representations for Person Re-identification
Zhao, L.-M., Li, X., Zhuang, Y., & Wang, J. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.349 · Cited by 834 · see citing works
People-tracking-by-detection and people-detection-by-tracking
Andriluka, M., Roth, S., & Schiele, B. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587583 · Cited by 825 · see citing works
UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking
Wen, L., Du, D., Cai, Z., Lei, Z., Chang, M.-C., Qi, H., Lim, J., Yang, M.-H., & Lyu, S. (2020) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2020.102907 · Cited by 804 · see citing works
Object Detection via a Multi-region and Semantic Segmentation-Aware CNN Model
Gidaris, S., & Komodakis, N. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.135 · Cited by 801 · see citing works
Globally-optimal greedy algorithms for tracking a variable number of objects
Pirsiavash, H., Ramanan, D., & Fowlkes, C. C. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995604 · Cited by 790 · see citing works
2015 IEEE International Conference on Image Processing (ICIP)
(2015) · doi:10.1109/icip30522.2015 · Cited by 726 · see citing works
Learning Color Names for Real-World Applications
van de Weijer, J., Schmid, C., Verbeek, J. J., & Larlus, D. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2019809 · Cited by 710 · see citing works
Hu, P., & Ramanan, D. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.166 · Cited by 709 · see citing works
Learning to Track: Online Multi-object Tracking by Decision Making
Yu, X., Alahi, A., & Savarese, S. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.534 · Cited by 684 · see citing works
Multiple Hypothesis Tracking Revisited
Kim, C., Li, F., Ciptadi, A., & Rehg, J. M. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.533 · Cited by 666 · see citing works
Large Margin Object Tracking with Circulant Feature Maps
Wang, M., Liu, Y., & Huang, Z. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.510 · Cited by 665 · see citing works
Yang, F., Choi, W., & Lin, Y. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.234 · Cited by 608 · see citing works
Fast Keypoint Recognition Using Random Ferns
Özuysal, M., Calonder, M., Lepetit, V., & Fua, P. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.23 · Cited by 594 · see citing works
Tracking the Untrackable: Learning to Track Multiple Cues with Long-Term Dependencies
Sadeghian, A., Alahi, A., & Savarese, S. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.41 · Cited by 567 · see citing works
Multiple People Tracking by Lifted Multicut and Person Re-identification
Tang, S., Andriluka, M., Andres, B., & Schiele, B. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.394 · Cited by 558 · see citing works
End-to-End People Detection in Crowded Scenes
Stewart, R., Andriluka, M., & Ng, A. Y. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.255 · Cited by 539 · see citing works
Chen, L., Ai, H., Zhuang, Z., & Shang, C. (2018) · 2018 IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2018.8486597 · Cited by 530 · see citing works
Pedestrian detection at 100 frames per second
Benenson, R., Mathias, M., Timofte, R., & Gool, L. V. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6248017 · Cited by 524 · see citing works
Influence sets based on reverse nearest neighbor queries
Korn, F. R., & Muthukrishnan, S. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335415 · Cited by 523 · see citing works
Online Multi-Target Tracking Using Recurrent Neural Networks
Milan, A., Rezatofighi, S. H., Dick, A., Reid, I. D., & Schindler, K. (2017) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v31i1.11194 · Cited by 514 · see citing works
Monocular 3D pose estimation and tracking by detection
Andriluka, M., Roth, S., & Schiele, B. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5540156 · Cited by 507 · see citing works
Robust Object Tracking by Hierarchical Association of Detection Responses
Huang, C., Wu, B., & Nevatia, R. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88688-4_58 · Cited by 501 · see citing works
PETS2009: Dataset and challenge
Ferryman, J. M., & Shahrokni, A. (2009) · doi:10.1109/pets-winter.2009.5399556 · Cited by 495 · see citing works
POI: Multiple Object Tracking with High Performance Detection and Appearance Feature
Yu, F., Li, W., Li, Q., Liu, Y., Shi, X., & Yan, J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48881-3_3 · Cited by 482 · see citing works
Learning by Tracking: Siamese CNN for Robust Target Association
Leal-Taixe, L., Canton-Ferrer, C., & Schindler, K. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2016.59 · Cited by 444 · see citing works
TTS synthesis with bidirectional LSTM based recurrent neural networks
Fan, Y., Qian, Y., Xie, F., & Soong, F. K. (2014) · Interspeech (USB) · doi:10.21437/interspeech.2014-443 · Cited by 438 · see citing works
Chu, Q., Ouyang, W., Li, H., Wang, X., Liu, B., & Yu, N. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.518 · Cited by 406 · see citing works
Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
(2004) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2004.1315128 · Cited by 381 · see citing works
Online Multi-Object Tracking with Dual Matching Attention Networks
Zhu, J., Yang, H., Liu, N., Kim, M., Zhang, W., & Yang, M.-H. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01228-1_23 · Cited by 377 · see citing works
Proceedings of the 17th International Conference on Pattern Recognition ICPR 2004
(2004) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2004.1334435 · Cited by 345 · see citing works
Regionlets for Generic Object Detection
Wang, X., Yang, M., Shenghuo Zhu, B., & Lin, Y. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.10 · Cited by 319 · see citing works
A unifying framework for detecting outliers and change points from time series
Takeuchi, J., & Yamanishi, K. (2006) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2006.1599387 · Cited by 316 · see citing works
Accurate Single Stage Detector Using Recurrent Rolling Convolution
Ren, J., Chen, X., Liu, J., Sun, W., Pang, J., Yan, Q., Tai, Y.-W., & Xu, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.87 · Cited by 305 · see citing works
Subcategory-Aware Convolutional Neural Networks for Object Proposals and Detection
Yu, X., Choi, W., Lin, Y., & Savarese, S. (2017) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2017.108 · Cited by 303 · see citing works
Recurrent Autoregressive Networks for Online Multi-object Tracking
Fang, K., Yu, X., Li, X., & Savarese, S. (2018) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2018.00057 · Cited by 278 · see citing works
DeepMatching: Hierarchical Deformable Dense Matching
Revaud, J., Weinzaepfel, P., Harchaoui, Z., & Schmid, C. (2016) · International Journal of Computer Vision · doi:10.1007/s11263-016-0908-3 · Cited by 274 · see citing works
Bae, S., & Yoon, K. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2691769 · Cited by 270 · see citing works
(2001) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2001.990448 · Cited by 253 · see citing works
Multi-object Tracking with Neural Gating Using Bilinear LSTM
Kim, C., Li, F., & Rehg, J. M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01237-3_13 · Cited by 248 · see citing works
Multi-object Tracking with Quadruplet Convolutional Neural Networks
Son, J., Baek, M., Cho, M., & Han, B. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.403 · Cited by 245 · see citing works
Online Multi-target Tracking with Strong and Weak Detections
Sánchez-Matilla, R., Poiesi, F., & Cavallaro, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48881-3_7 · Cited by 242 · see citing works
Tracking of Multiple, Partially Occluded Humans based on Static Body Part Detection
Wu, B., & Nevatia, R. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.312 · Cited by 227 · see citing works
Leal-Taixé, L., Pons‐Moll, G., & Rosenhahn, B. (2011) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2011.6130233 · Cited by 212 · see citing works
Efficient Coarse-to-Fine Patch Match for Large Displacement Optical Flow
Hu, Y., Song, R., & Li, Y. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.615 · Cited by 193 · see citing works
Multi-person Tracking by Multicut and Deep Matching
Tang, S., Andres, B., Andriluka, M., & Schiele, B. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48881-3_8 · Cited by 182 · see citing works
Multiple Target Tracking Based on Undirected Hierarchical Relation Hypergraph
Wen, L., Li, W., Yan, J., Lei, Z., Yi, D., & Li, S. Z. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.167 · Cited by 174 · see citing works
Beyond Pixels: Leveraging Geometry and Shape Cues for Online Multi-Object Tracking
Sharma, S., Ansari, J. A., Jatavallabhula, K. M., & Krishna, K. M. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8461018 · Cited by 169 · see citing works
Deeply-Learned Part-Aligned Representations for Person Re-IdentificationPreprint
Zhao, L.-M., Li, X., Wang, J., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.07256 · Cited by 165 · see citing works
Multimodal Technologies for Perception of Humans
Stiefelhagen, R., & Garofolo, J. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-69568-4 · Cited by 164 · see citing works
Multi-target tracking using CNN-based features: CNNMTT
Mahmoudi, N., Ahadi, S. M., & Rahmati, M. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6467-6 · Cited by 163 · see citing works
Automatic Individual Pig Detection and Tracking in Pig Farms
Zhang, L., Gray, H. E., Ye, X., Collins, L., & Allinson, N. M. (2019) · Sensors · doi:10.3390/s19051188 · Cited by 156 · see citing works
Fusion of Head and Full-Body Detectors for Multi-object Tracking
Henschel, R. D., Leal-Taixé, L., Cremers, D., & Rosenhahn, B. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00192 · Cited by 152 · see citing works
Multi-class Multi-object Tracking Using Changing Point Detection
Lee, B., Erdenee, E., Jin, S., Nam, M. Y., Jung, Y. G., & Rhee, P. K. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48881-3_6 · Cited by 151 · see citing works
Visual tracking of numerous targets via multi-Bernoulli filtering of image data
Hoseinnezhad, R., Vo, B., Vo, B.-T., & Suter, D. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.04.004 · Cited by 149 · see citing works
Enhancing Detection Model for Multiple Hypothesis Tracking
Chen, J., Sheng, H., Zhang, Y., & Xiong, Z. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.266 · Cited by 146 · see citing works
UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and TrackingPreprint
Wen, L., Du, D., Cai, Z., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.04136 · Cited by 142 · see citing works
Online Video Object Detection Using Association LSTM
Lu, Y., Lu, C., & Tang, C. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.257 · Cited by 139 · see citing works
Online Multi-Object Tracking With Instance-Aware Tracker and Dynamic Model Refreshment
Chu, P., Fan, H., Tan, C. C., & Ling, H. (2019) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2019.00023 · Cited by 129 · see citing works
Tracking the Trackers: An Analysis of the State of the Art in Multiple Object TrackingPreprint
Leal-Taixé, L., Milan, A., Schindler, K., Cremers, D., Reid, I. D., & Roth, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.02781 · Cited by 127 · see citing works
Heterogeneous Association Graph Fusion for Target Association in Multiple Object Tracking
Sheng, H., Zhang, Y., Chen, J., Xiong, Z., & Zhang, J. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2882192 · Cited by 123 · see citing works
Efficient Decomposition of Image and Mesh Graphs by Lifted Multicuts
Keuper, M., Levinkov, E., Bonneel, N., Lavoué, G., Brox, T., & Andres, B. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.204 · Cited by 121 · see citing works
A model for fine-grained vehicle classification based on deep learning
Yu, S., Wu, Y., Li, W., Song, Z., & Zeng, W. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.09.116 · Cited by 119 · see citing works
Online multi-object tracking with convolutional neural networks
Chen, L., Ai, H., Shang, C., Zhuang, Z., & Bai, B. B. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8296360 · Cited by 118 · see citing works
Proceedings of the ... Scandinavian Conference on Image Analysis
Bildanalys, S. S. F. A. (1980) · Medical Entomology and Zoology · Cited by 114 · see citing works
Online Multi-Target Tracking with Tensor-Based High-Order Graph Matching
Zhou, Z., Xing, J., Zhang, M., & Hu, W. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8545450 · Cited by 111 · see citing works
Wang, B., Wang, L., Shuai, B., Zuo, Z. C., Liu, T., Chan, K. L., & Wang, G. (2016) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2016.55 · Cited by 110 · see citing works
Machine Learning Methods for Data Association in Multi-Object Tracking
Emami, P., Pardalos, P. M., Elefteriadou, L., & Ranka, S. (2020) · ACM Computing Surveys · doi:10.1145/3394659 · Cited by 104 · see citing works
Ma, C., Yang, C., Yang, F., Zhuang, Y., Zhang, Z., Jia, H., & Xie, X. (2018) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2018.8486454 · Cited by 103 · see citing works
Marchi, E., Ferroni, G., Eyben, F., Gabrielli, L., Squartini, S., & Schuller, B. W. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6853982 · Cited by 93 · see citing works
Collaborative Deep Reinforcement Learning for Multi-object Tracking
Ren, L., Lu, J., Wang, Z., Tian, Q. C., & Zhou, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01219-9_36 · Cited by 93 · see citing works
Softmax to Softassign: neural network algorithms for combinatorial optimization
Gold, S., & Rangarajan, A. (1996) · Cited by 85 · see citing works
Zhou, H., Ouyang, W., Cheng, J., Wang, X., & Li, H. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2825679 · Cited by 84 · see citing works
Deep self-paced learning for person re-identification
Zhou, S., Wang, J., Meng, D., Xin, X., Li, Y., Gong, Y., & Zheng, N. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.10.005 · Cited by 78 · see citing works
Multiple Object Tracking via Feature Pyramid Siamese Networks
Lee, S., & Kim, E. (2018) · IEEE Access · doi:10.1109/access.2018.2889442 · Cited by 73 · see citing works
Min, W., Fan, M., Guo, X., & Han, Q. (2017) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2756989 · Cited by 71 · see citing works
Softmax to Softassign: neural network algorithms for combinatorial optimization
GoldSteven, & RangarajanAnand (1996) · doi:10.5555/235912.235919 · Cited by 70 · see citing works
Automatically Detect and Track Multiple Fish Swimming in Shallow Water with Frequent Occlusion
Qian, Z.-M., Cheng, X. E., & Chen, Y. Q. (2014) · PLoS ONE · doi:10.1371/journal.pone.0106506 · Cited by 68 · see citing works
Data Association for Multi-Object Tracking via Deep Neural Networks
Yoon, K., Kim, D. Y., Yoon, Y.-C., & Jeon, M. (2019) · Sensors · doi:10.3390/s19030559 · Cited by 68 · see citing works
A Directed Sparse Graphical Model for Multi-target Tracking
Ullah, M., & Cheikh, F. A. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00235 · Cited by 66 · see citing works
A Hybrid Data Association Framework for Robust Online Multi-Object Tracking
Yang, M., Wu, Y., & Jia, Y. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2745103 · Cited by 63 · see citing works
CVPR19 Tracking and Detection Challenge: How crowded can it get?Preprint
Dendorfer, P., Rezatofighi, H., Milan, A., Shi, Q., Cremers, D., Reid, I. D., Roth, S., Schindler, K., & Leal-Taixé, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.04567 · Cited by 63 · see citing works
Learning Transformational Invariants from Natural Movies
Cadieu, C. F., & Olshausen, B. A. (2008) · Cited by 62 · see citing works
Robust tracking of fish schools using CNN for head identification
Wang, S. H., Zhao, J., & Chen, Y. Q. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-4045-3 · Cited by 61 · see citing works
Multi-Object Tracking with Correlation Filter for Autonomous Vehicle
Zhao, D., Fu, H., Xiao, L., Wu, T., & Dai, B. (2018) · Sensors · doi:10.3390/s18072004 · Cited by 57 · see citing works
Multiple human tracking in RGB‐depth data: a survey
Camplani, M., Paiement, A., Mirmehdi, M., Damen, D., Hannuna, S., Burghardt, T., & Tao, L. (2016) · IET Computer Vision · doi:10.1049/iet-cvi.2016.0178 · Cited by 55 · see citing works
Machine Learning Methods for Solving Assignment Problems in Multi-Target TrackingPreprint
Emami, P., Pardalos, P. M., Elefteriadou, L., & Ranka, S. (2018) · arXiv (Cornell University) · Cited by 53 · see citing works
Customized Multi-person Tracker
Ma, L., Tang, S., Black, M. J., & Gool, L. V. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20890-5_39 · Cited by 48 · see citing works
Tracking Persons-of-Interest via Adaptive Discriminative Features
Zhang, S., Gong, Y., Huang, J., Lim, J., Wang, J., Ahuja, N., & Yang, M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46454-1_26 · Cited by 42 · see citing works
Xiang, J., Zhang, G., & Hou, J. (2019) · IEEE Access · doi:10.1109/access.2019.2901520 · Cited by 41 · see citing works
Deep Feature Based End-to-End Transportation Network for Multi-Target Tracking
Ullah, M., & Cheikh, F. A. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451472 · Cited by 40 · see citing works
Learning Non-Uniform Hypergraph for Multi-Object Tracking
Wen, L., Du, D., Li, S., Bian, X., & Lyu, S. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33018981 · Cited by 40 · see citing works
Joint Detection and Online Multi-object Tracking
Kieritz, H., Hübner, W., & Arens, M. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00195 · Cited by 37 · see citing works
A hierarchical feature model for multi-target tracking
Ullah, M., Mohammed, A. K., Cheikh, F. A., & Wang, Z. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8296755 · Cited by 33 · see citing works
Memory Based Online Learning of Deep Representations from Video Streams
Pernici, F., Bartoli, F., Bruni, M., & Bimbo, A. D. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00247 · Cited by 31 · see citing works
Occlusion Handling in Tracking Multiple People Using RNN
Babaee, M., Li, Z., & Rigoll, G. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451140 · Cited by 31 · see citing works
Similarity Mapping with Enhanced Siamese Network for Multi-Object TrackingPreprint
Kim, M., Alletto, S., & Rigazio, L. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.09156 · Cited by 30 · see citing works
Ran, N., Kong, L., Wang, Y., & Liu, Q. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-05710-7_34 · Cited by 30 · see citing works
Online CNN-based multiple object tracking with enhanced model updates and identity association
Gan, W., Wang, S., Lei, X., Lee, M., & Kuo, C. ‐. J. (2018) · Signal Processing Image Communication · doi:10.1016/j.image.2018.05.008 · Cited by 29 · see citing works
An Online and Flexible Multi-object Tracking Framework Using Long Short-Term Memory
Wan, X., Wang, J., & Zhou, S. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00169 · Cited by 28 · see citing works
GM-PHD Filter Based Online Multiple Human Tracking Using Deep Discriminative Correlation Matching
Fu, Z., Angelini, F., Naqvi, S. M., & Chambers, J. A. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8461946 · Cited by 26 · see citing works
Online Tracker Optimization for Multi-Pedestrian Tracking Using a Moving Vehicle Camera
Kim, S., Nam, J.-Y., & Ko, B. C. (2018) · IEEE Access · doi:10.1109/access.2018.2867621 · Cited by 25 · see citing works
Interpolation-Based Object Detection Using Motion Vectors for Embedded Real-time Tracking Systems
Ujiie, T., Hiromoto, M., & Sato, T. G. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00104 · Cited by 23 · see citing works
Learning deep features for multiple object tracking by using a multi-task learning strategy
Wang, L., Pham, N. T., Ng, T.-T., Wang, G., Chan, K. L., & Leman, K. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7025168 · Cited by 22 · see citing works
LSTM Multiple Object Tracker Combining Multiple Cues
Liang, Y., & Zhou, Y. F. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451739 · Cited by 21 · see citing works
Instance flow based online multiple object tracking
Bullinger, S., Bodensteiner, C., & Arens, M. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8296388 · Cited by 20 · see citing works
Multi-Agent Reinforcement Learning for Multi-Object Tracking
Rosello, P., & Kochenderfer, M. J. (2018) · Adaptive Agents and Multi-Agents Systems · doi:10.65109/bhwq1301 · Cited by 20 · see citing works
Online multiple object tracking via flow and convolutional features
Wang, L., Xu, L., Kim, M. Y., Rigazico, L., & Yang, M. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8296959 · Cited by 17 · see citing works
Recurrent Metric Networks and Batch Multiple Hypothesis for Multi-Object Tracking
Chen, L., Peng, X., & Ren, M. (2018) · IEEE Access · doi:10.1109/access.2018.2889187 · Cited by 17 · see citing works
Decision Controller for Object Tracking With Deep Reinforcement Learning
Zhong, Z., Yang, Z., Feng, W., Wu, W., Hu, Y., & Liu, C. (2019) · IEEE Access · doi:10.1109/access.2019.2900476 · Cited by 17 · see citing works
Improving Global Multi-target Tracking with Local Updates
Milan, A., Gade, R., Dick, A., Moeslund, T. B., & Reid, I. D. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16199-0_13 · Cited by 13 · see citing works
Eliminating Exposure Bias and Loss-Evaluation Mismatch in Multiple Object TrackingPreprint
Maksai, A., & Fua, P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.10984 · Cited by 13 · see citing works
Multi-target tracking by non-linear motion patterns based on hierarchical network flows
Lu, W., Zhou, Z., Zhang, L., & Zheng, G. (2019) · Multimedia Systems · doi:10.1007/s00530-019-00614-y · Cited by 13 · see citing works
Lifting of MulticutsPreprint
Andres, B. (2015) · arXiv (Cornell University) · Cited by 10 · see citing works
Jiang, Y., Shin, H., & Ko, H. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8462063 · Cited by 10 · see citing works
Probability hypothesis density filter using determinantal point processes for multi object tracking
Jorquera, F., Hernández, S., & Vergara, D. (2019) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2019.04.001 · Cited by 10 · see citing works
Who did What at Where and When: Simultaneous Multi-Person Tracking and Activity RecognitionPreprint
Li, W., Chang, M., & Lyu, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.01253 · Cited by 9 · see citing works
Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods
Hoak, A., Medeiros, H. P., & Povinelli, R. J. (2017) · Sensors · doi:10.3390/s17030501 · Cited by 8 · see citing works
An Automatic Tracking Method for Multiple Cells Based on Multi-Feature Fusion
Hu, H., Zhou, L., Guan, Q., Zhou, Q., & Chen, S. (2018) · IEEE Access · doi:10.1109/access.2018.2880563 · Cited by 8 · see citing works
SOT for MOTPreprint
He, Q., Wu, J., Yu, G., & Zhang, C. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.01059 · Cited by 7 · see citing works
Multi-person tracking by discriminative affinity model and hierarchical association
Li, M., Liu, Z., Xiong, Y., & Zheng, L. (2017) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/compcomm.2017.8322838 · Cited by 3 · see citing works
Flagged first, then by citations
7 of the 199 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- [17][17] Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision , pages 2961-2969, 2017. -
- [22][22] Rainer Stiefelhagen and John Garofolo. Multimodal Technologies for Perception of Humans: First International Evaluation Workshop on Classification of Events, Activities and Relationships, CLEAR 2006, Southampton, UK, April 6-7, 2006, Revised Selected Papers , volume 4122. Springer, 2007. -
- [96][96] Kuan Fang, Yu Xiang, Xiaocheng Li, and Silvio Savarese. Recurrent autoregressive networks for online multiobject tracking. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) , pages 466-475. IEEE, 2018. -
Cited by
Sorted by citations
Detection and tracking meet drones challenge
Zhu, P., Wen, L., Du, D., Bian, X., Fan, H., Hu, Q., & Ling, H. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3119563 · Cited by 1,336 · see citing works
Bayoudh, K., Knani, R., Hamdaoui, F., & Mtibaa, A. (2022) · The Visual Computer · doi:10.1007/s00371-021-02166-7 · Cited by 456 · see citing works
A survey of sound source localization with deep learning methods
Grumiaux, P.-A., Kitić, S., Laurent Girin, B., & Guérin, A. (2022) · The Journal of the Acoustical Society of America · doi:10.1121/10.0011809 · Cited by 365 · see citing works
Manakitsa, N., Maraslidis, G. S., Moysis, L., & Fragulis, G. F. (2024) · Technologies · doi:10.3390/technologies12020015 · Cited by 326 · see citing works
Quantifying behavior to understand the brain
Pereira, T., Shaevitz, J. W., & Murthy, M. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-00734-z · Cited by 302 · see citing works
Human Memory Update Strategy: A Multi-Layer Template Update Mechanism for Remote Visual Monitoring
Liu, S., Wang, S., Liu, X., Gandomi, A. H., Daneshmand, M., Muhammad, K., & de Albuquerque, V. H. C. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3065580 · Cited by 255 · see citing works
RelationTrack: Relation-Aware Multiple Object Tracking With Decoupled Representation
Yu, E., Li, Z., Han, S., & Wang, H. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3150169 · Cited by 219 · see citing works
A Survey of Multiple Pedestrian Tracking Based on Tracking-by-Detection Framework
Sun, Z., Chen, J., Liang, C., Ruan, W., & Mukherjee, M. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.3009717 · Cited by 181 · see citing works
Li, Y., Yabuki, N., & Fukuda, T. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.103656 · Cited by 164 · see citing works
A review of deep learning-based detection methods for COVID-19
Subramanian, N., Elharrouss, O., Al-Maadeed, S. A., & Chowdhury, M. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105233 · Cited by 154 · see citing works
Bakırcı, M. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112015 · Cited by 153 · see citing works
Belhadi, A., Djenouri, Y., Srivastava, G., Djenouri, D., Lin, J. C., & Fortino, G. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.08.003 · Cited by 148 · see citing works
Video Processing Using Deep Learning Techniques: A Systematic Literature Review
Sharma, V., Gupta, M., Kumar, A., & Mishra, D. (2021) · IEEE Access · doi:10.1109/access.2021.3118541 · Cited by 143 · see citing works
Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook
Goodwin, M., Halvorsen, K. T., Jiao, L., Knausgård, K. M., Martin, A. H., Moyano, M., Oomen, R. A., Rasmussen, J. H., Sørdalen, T. K., & Thorbjørnsen, S. H. (2021) · ICES Journal of Marine Science · doi:10.1093/icesjms/fsab255 · Cited by 141 · see citing works
Learning a Proposal Classifier for Multiple Object Tracking
Dai, P., Weng, R., Choi, W., Zhang, C., He, Z., & Ding, W. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00247 · Cited by 130 · see citing works
Pauly, L., Rharbaoui, W., Shneider, C., Rathinam, A., Gaudillière, V., & Aouada, D. (2023) · Acta Astronautica · doi:10.1016/j.actaastro.2023.08.001 · Cited by 121 · see citing works
“The use of DL has had significant implications in developing computer vision algorithms over the last decade [166], [22], improving their performance and robustness for applications such as image classification [173], segmentation [99], and object tracking [26].”
MethodPereira, R., Carvalho, G., Garrote, L., & Nunes, U. (2022) · Applied Sciences · doi:10.3390/app12031319 · Cited by 116 · see citing works
Automatic recognition of feeding and foraging behaviour in pigs using deep learning
Alameer, A., Kyriazakis, I., Dalton, H. A., Miller, A. L., & Bacardit, J. (2020) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2020.06.013 · Cited by 111 · see citing works
Alameer, A., Kyriazakis, I., & Bacardit, J. (2020) · Scientific Reports · doi:10.1038/s41598-020-70688-6 · Cited by 110 · see citing works
Tracking Pedestrian Heads in Dense Crowd
Sundararaman, R., Braga, C. D. A., Marchand, É., & Pettré, J. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00386 · Cited by 110 · see citing works
Deep Learning in Visual Tracking: A Review
Jiao, L., Wang, D., Bai, Y., Chen, P., & Liu, F. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3136907 · Cited by 110 · see citing works
Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques
Xu, R., Razavi, S., & Zheng, R. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3323091 · Cited by 109 · see citing works
Real-time multiple object tracking using deep learning methods
Meimetis, D., Daramouskas, I., Perikos, I., & Hatzilygeroudis, I. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06391-y · Cited by 106 · see citing works
Sánchez, F. L., Hupont, I., Tabik, S., & Herrera, F. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.07.008 · Cited by 104 · see citing works
Self-citationMachine Learning Methods for Data Association in Multi-Object Tracking
Emami, P., Pardalos, P. M., Elefteriadou, L., & Ranka, S. (2020) · ACM Computing Surveys · doi:10.1145/3394659 · Cited by 104 · see citing works
Li, Y., Yabuki, N., & Fukuda, T. (2022) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2022.104603 · Cited by 101 · see citing works
Automated sewer pipe defect tracking in CCTV videos based on defect detection and metric learning
Wang, M., Kumar, S. S., & Cheng, J. C. P. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103438 · Cited by 99 · see citing works
Scherr, T., Löffler, K., Böhland, M., & Mikut, R. (2020) · PLoS ONE · doi:10.1371/journal.pone.0243219 · Cited by 98 · see citing works
On-Road Object Detection and Tracking Based on Radar and Vision Fusion: A Review
Tang, X., Zhang, Z., & Qin, Y. (2021) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2021.3093379 · Cited by 98 · see citing works
Chu, W., Qiqige, W., Du, X., Xiong, Q., Huang, T., & Li, K. (2021) · Chinese Journal of Mechanical Engineering · doi:10.1186/s10033-021-00638-4 · Cited by 97 · see citing works
Unsupervised deep clustering via adaptive GMM modeling and optimization
Wang, J., & Jiang, J. (2021) · Neurocomputing · doi:10.1016/j.neucom.2020.12.082 · Cited by 96 · see citing works
Computer Vision Enabled Building Digital Twin Using Building Information Model
Zhou, X., Sun, K., Wang, J., Zhao, J., Feng, C., Yang, Y., & Zhou, W. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3190366 · Cited by 96 · see citing works
Ahmed, I., Din, S., Jeon, G., Piccialli, F., & Fortino, G. (2021) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2020.1003453 · Cited by 92 · see citing works
Recent advances of single-object tracking methods: A brief survey
Zhang, Y., Wang, T., Liu, K., Zhang, B., & Chen, L. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.05.011 · Cited by 88 · see citing works
Moving Target Tracking by Unmanned Aerial Vehicle: A Survey and Taxonomy
Sun, N., Zhao, J., Shi, Q., Liu, C., & Liu, P. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3363084 · Cited by 88 · see citing works
Transformers in Single Object Tracking: An Experimental Survey
Kugarajeevan, J., Thanikasalam, K., Ramanan, A., & Fernando, S. (2023) · IEEE Access · doi:10.1109/access.2023.3298440 · Cited by 86 · see citing works
Survey of State-of-Art Autonomous Driving Technologies with Deep Learning
Huang, Y., & Chen, Y. (2020) · IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C) · doi:10.1109/qrs-c51114.2020.00045 · Cited by 81 · see citing works
Automated Tracking Systems for the Assessment of Farmed Poultry
Neethirajan, S. R. (2022) · Animals · doi:10.3390/ani12030232 · Cited by 81 · see citing works
DEFT: Detection Embeddings for TrackingPreprint
Chaâbane, M. A., Zhang, P., Beveridge, J. R., & O′Hara, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.02267 · Cited by 80 · see citing works
End-to-End Learning Deep CRF Models for Multi-Object Tracking Deep CRF Models
Xiang, J., Xu, G., Ma, C., & Hou, J. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.2975842 · Cited by 77 · see citing works
Xiao, B., Lin, Q., & Chen, Y. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103721 · Cited by 66 · see citing works
Vukićević, A. M., Petrovic, M., Milošević, P., Peulić, A., Jovanovic, K. M., & Novakovic, A. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10978-x · Cited by 76 · see citing works
Multi-Object Pedestrian Tracking Using Improved YOLOv8 and OC-SORT
Xiao, X. P., & Feng, X. (2023) · Sensors · doi:10.3390/s23208439 · Cited by 74 · see citing works
A Review of Deep Learning-Based Visual Multi-Object Tracking Algorithms for Autonomous Driving
Guo, S., Wang, S., Yang, Z., Wang, L., Zhang, H., Guo, P., Gao, Y., & Guo, J. (2022) · Applied Sciences · doi:10.3390/app122110741 · Cited by 73 · see citing works
Simple Unsupervised Multi-Object TrackingPreprint
Karthik, S., Prabhu, A., & Gandhi, V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.02609 · Cited by 72 · see citing works
Recent advances in the application of deep learning methods to forestry
Wang, Y., Zhang, W., Gao, R. H., Zheng, J., & Wang, X. (2021) · Wood Science and Technology · doi:10.1007/s00226-021-01309-2 · Cited by 68 · see citing works
Sequential attention mechanism for weakly supervised video anomaly detection
Ullah, W., Ullah, F. U. M., Khan, Z. A., & Baik, S. W. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120599 · Cited by 67 · see citing works
Review on Vehicle Detection Technology for Unmanned Ground Vehicles
Liu, Q., Li, Z., Yuan, S., Zhu, Y., & Li, X. (2021) · Sensors · doi:10.3390/s21041354 · Cited by 66 · see citing works
MLVSNet: Multi-level Voting Siamese Network for 3D Visual Tracking
Wang, Z., Xie, Q., Lai, Y., Wu, J., Long, K., & Wang, J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00309 · Cited by 64 · see citing works
Zhou, W., Liu, C., Lei, J., Yu, L., & Luo, T. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.11.100 · Cited by 64 · see citing works
Iftikhar, S., Asim, M., Zhang, Z., Muthanna, A., Chen, J., ELAffendi, M. A., Sedik, A., & El‐Latif, A. A. A. (2023) · Applied Sciences · doi:10.3390/app13063995 · Cited by 63 · see citing works
Velastín, S. A., Fernández, R., Espinosa, J. E., & Bay, A. (2020) · Sensors · doi:10.3390/s20216251 · Cited by 62 · see citing works
Tracking People by Predicting 3D Appearance, Location and Pose
Rajasegaran, J., Pavlakos, G., Kanazawa, A., & Malik, J. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00276 · Cited by 62 · see citing works
Two-stage three-way enhanced technique for ensemble learning in inclusive policy text classification
Liang, D., & Yi, B. (2020) · Information Sciences · doi:10.1016/j.ins.2020.08.051 · Cited by 61 · see citing works
A survey of detection-based video multi-object tracking
Dai, Y., Hu, Z., Zhang, S., & Liu, L. (2022) · Displays · doi:10.1016/j.displa.2022.102317 · Cited by 59 · see citing works
Real-time neural network inference on extremely weak devices
Huang, K., & Gao, W. (2022) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/3495243.3560551 · Cited by 58 · see citing works
Artificial intelligence for fish behavior recognition may unlock fishing gear selectivity
Abangan, A. S., Kopp, D., & Faillettaz, R. (2023) · Frontiers in Marine Science · doi:10.3389/fmars.2023.1010761 · Cited by 58 · see citing works
Chen, X., Lowerison, M. R., Dong, Z., Sekaran, N. V. C., Huang, C., Chen, S., Fan, T. M., Llano, D. A., & Song, P. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3251197 · Cited by 58 · see citing works
Multi-object tracking review: retrospective and emerging trend
Guan, Z., Wang, Z., Zhang, G., Li, L., Zhang, M., Shi, Z., & Jiang, N. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11212-y · Cited by 58 · see citing works
Revolutionizing optical imaging: computational imaging via deep learning
Luo, X., Wang, S., Liu, J., Dong, X. W., He, P., Yang, Q., Chen, X., Zhou, F., Zhang, T., Feng, S., et al. (2025) · Photonics Insights · doi:10.3788/pi.2025.r03 · Cited by 58 · see citing works
Bui, K. N., Yi, H., & Cho, J.-H. (2020) · Energies · doi:10.3390/en13082036 · Cited by 58 · see citing works
GCNNMatch: Graph Convolutional Neural Networks for Multi-Object Tracking via Sinkhorn NormalizationPreprint
Papakis, I., Sarkar, A., & Karpatne, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.00067 · Cited by 58 · see citing works
Multiple Object Tracking in Deep Learning Approaches: A Survey
Park, Y., Dang, L. M., Lee, S., Han, D., & Moon, H. (2021) · Electronics · doi:10.3390/electronics10192406 · Cited by 57 · see citing works
AnimalTrack: A Benchmark for Multi-Animal Tracking in the Wild
Zhang, L., Gao, J., Xiao, Z., & Fan, H. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01711-8 · Cited by 57 · see citing works
Physics-Informed Supervised Residual Learning for Electromagnetic Modeling
Shan, T., Zeng, J., Song, X., Guo, R., Li, M., Yang, F., & Xu, S. (2023) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.2023.3245281 · Cited by 57 · see citing works
Mauri, A., Khemmar, R., Decoux, B., Ragot, N., Rossi, R., Trabelsi, R., Boutteau, R., Ertaud, J.-Y., & Savatier, X. (2020) · Sensors · doi:10.3390/s20020532 · Cited by 57 · see citing works
Benjdira, B., Koubâa, A., Azar, A. T., Khan, Z., Ammar, A., & Boulila, W. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105095 · Cited by 56 · see citing works
Autonomous Driving with Deep Learning: A Survey of State-of-Art TechnologiesPreprint
Huang, Y., & Chen, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.06091 · Cited by 56 · see citing works
Similarity based person re-identification for multi-object tracking using deep Siamese network
Suljagic, H., Bayraktar, E., & Çelebi, N. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07456-2 · Cited by 54 · see citing works
Wong, P. K.-Y., Luo, H., Wang, M., Leung, P. H., & Cheng, J. C. P. (2021) · Advanced Engineering Informatics · doi:10.1016/j.aei.2021.101356 · Cited by 53 · see citing works
Near-Field Perception for Low-Speed Vehicle Automation Using Surround-View Fisheye Cameras
Eising, C., Horgan, J., & Yogamani, S. K. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3127646 · Cited by 53 · see citing works
He, Y., Li, T., Ge, R., Yang, J., Kong, Y., Zhu, J., Shu, H., Yang, G., & Li, S. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3095409 · Cited by 52 · see citing works
A survey of unmanned aerial vehicles and deep learning in precision agriculture
Wang, D., Zhao, M., Li, Z., Xu, S., Wu, X., Ma, X., & Liu, X. (2024) · European Journal of Agronomy · doi:10.1016/j.eja.2024.127477 · Cited by 52 · see citing works
Sugeno integral generalization applied to improve adaptive image binarization
Bardozzo, F., de la Osa, B., Horanská, Ľ., Fumanal-Idocin, J., Priscoli, M. D., Troiano, L., Tagliaferri, R., Fernández, J., & Bustince, H. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.10.020 · Cited by 52 · see citing works
Self-citationSelf-Supervised Small Soccer Player Detection and Tracking
Hurault, S., Ballester, C., & Haro, G. (2020) · ACM International Workshop on Multimedia Content Analysis in Sports (MMSports) · doi:10.1145/3422844.3423054 · Cited by 52 · see citing works
Wu, M., Qian, Y., Wang, C., & Yang, M. Z. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00460 · Cited by 51 · see citing works
Inflated 3D ConvNet context analysis for violence detection
Freire-Obregón, D., Barra, P., Castrillón-Santana, M., & Marsico, M. D. (2021) · Machine Vision and Applications · doi:10.1007/s00138-021-01264-9 · Cited by 50 · see citing works
Opportunities and challenges for deep learning in cell dynamics research
Chai, B., Efstathiou, C., Yue, H., & Draviam, V. M. (2023) · Trends in Cell Biology · doi:10.1016/j.tcb.2023.10.010 · Cited by 50 · see citing works
An Accurate and Fast Animal Species Detection System for Embedded Devices
Ibraheam, M., Li, K. F., & Gebali, F. (2023) · IEEE Access · doi:10.1109/access.2023.3252499 · Cited by 48 · see citing works
GMOT-40: A Benchmark for Generic Multiple Object Tracking
Bai, H., Cheng, W., Chu, P., Liu, J., Zhang, K., & Ling, H. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00665 · Cited by 46 · see citing works
Self-supervised Multi-view Multi-Human Association and Tracking
Gan, Y., Han, R., Yin, L., Feng, W., & Wang, S. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475177 · Cited by 46 · see citing works
Zhang, T., Aftab, W., Mihaylova, L., Langran-Wheeler, C., Rigby, S. E., Fletcher, D. I., Maddock, S., & Bosworth, G. (2022) · Sensors · doi:10.3390/s22124324 · Cited by 46 · see citing works
A review of three dimensional reconstruction techniques
Phang, J. T. S., Lim, K. H., & Chiong, C. W. R. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-10605-9 · Cited by 45 · see citing works
A Multi-Object Tracking Algorithm With Center-Based Feature Extraction and Occlusion Handling
Cao, Z., Li, J., Zhang, D., Zhou, M., & Abusorrah, A. M. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3229978 · Cited by 45 · see citing works
Modern Image-Guided Surgery: A Narrative Review of Medical Image Processing and Visualization
Lin, Z., Lei, C., & Yang, L. (2023) · Sensors · doi:10.3390/s23249872 · Cited by 45 · see citing works
A Vehicle Counts by Class Framework using Distinguished Regions Tracking at Multiple Intersections
Bui, K. N., Yi, H., & Cho, J.-H. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00297 · Cited by 45 · see citing works
Deep Learning-Based Computer Vision for Surveillance in ITS: Evaluation of State-of-the-Art Methods
Xie, J., Zheng, Y., Du, R., Xiong, W., Cao, Y., Ma, Z., Cao, D., & Guo, J. H. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3065250 · Cited by 45 · see citing works
Yıldız, S., Aydemir, O., Memiş, A., & Varlı, S. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105032 · Cited by 44 · see citing works
Dubey, S., Boragule, A., Gwak, J., & Jeon, M. (2021) · Applied Sciences · doi:10.3390/app11031344 · Cited by 44 · see citing works
Löffler, K., & Mikut, R. (2022) · IEEE Access · doi:10.1109/access.2022.3192880 · Cited by 44 · see citing works
A Region-Based Deep Learning Approach to Automated Retail Checkout
Shoman, M., Aboah, A., Morehead, A., Duan, Y., Daud, A., & Adu-Gyamfi, Y. O. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00362 · Cited by 42 · see citing works
Le, V.-H. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13921-w · Cited by 40 · see citing works
Gaussian-response correlation filter for robust visual object tracking
Moorthy, S., Choi, J. Y., & Joo, Y. H. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.06.016 · Cited by 39 · see citing works
Zargari, A., Lodewijk, G. A., Mashhadi, N., Cook, N., Neudorf, C., Araghbidikashani, K., Hays, R., Kozuki, S., Rubio, S., Hrabeta‐Robinson, E., et al. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100500 · Cited by 39 · see citing works
A Review of Multi‐Object Tracking in Recent Times
Li, S., Ren, H., Xie, X., & Cao, Y. (2025) · IET Computer Vision · doi:10.1049/cvi2.70010 · Cited by 39 · see citing works
Priscoli, M. D., Memmolo, P., Ciaparrone, G., Bianco, V., Merola, F., Miccio, L., Bardozzo, F., Pirone, D., Mugnano, M., Cimmino, F., et al. (2021) · IEEE Journal of Selected Topics in Quantum Electronics · doi:10.1109/jstqe.2021.3059532 · Cited by 38 · see citing works
Self-citationXu, X., Feng, Z., Cao, C. C., Yu, C., Li, M., Wu, Z., Ye, S., & Shang, Y. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3213438 · Cited by 37 · see citing works
Kratsch, W., König, F., & Röglinger, M. (2022) · Decision Support Systems · doi:10.1016/j.dss.2022.113794 · Cited by 37 · see citing works
The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Counting
Kay, J., Kulits, P., Stathatos, S., Deng, S., Young, E., Beery, S., Horn, G. V., & Perona, P. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20074-8_17 · Cited by 37 · see citing works
MaskHunter: real‐time object detection of face masks during the COVID‐19 pandemic
Cao, Z., Shao, M., Xu, L., Mu, S., & Qu, H. (2020) · IET Image Processing · doi:10.1049/iet-ipr.2020.1119 · Cited by 36 · see citing works
- Authors
- Ciaparrone, G., Luque Sánchez, F., Tabik, S., Troiano, L., Tagliaferri, R., & Herrera, F.
- Affiliations
- University of Salerno (IT)
- Universidad de Granada (ES)
- University of Sannio (IT)
- Corresponding author
- Ciaparrone, Gioele
- ORCID
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2020-03
- Volume
- 381
- Pages
- 61-88
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2966535964
- Cited by
- 665
- References
- 199
- Citations by year
- 2026: 56 · 2025: 79 · 2024: 97 · 2023: 115 · 2022: 131 · 2021: 128 · 2020: 57 · 2019: 2
- FWCI
- 35.60
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Neurocomputing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Fundación BBVA
- Agencia Estatal de Investigación
- Ministerio de Ciencia y Tecnología
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- multi-target tracking, video tracking, deep learning, single-camera video, trajectory prediction, object detection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
