The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Detect to Track and Track to Detect
Open accessAbstract
Recent approaches for high accuracy detection and tracking of object categories in video consist of complex multistage solutions that become more cumbersome each year. In this paper we propose a ConvNet architecture that jointly performs detection and tracking, solving the task in a simple and effective way. Our contributions are threefold: (i) we set up a ConvNet architecture for simultaneous detection and tracking, using a multi-task objective for frame-based object detection and across-frame track regression; (ii) we introduce correlation features that represent object co-occurrences across time to aid the ConvNet during tracking; and (iii) we link the frame level detections based on our across-frame tracklets to produce high accuracy detections at the video level. Our ConvNet architecture for spatiotemporal object detection is evaluated on the large-scale ImageNet VID dataset where it achieves state-of-the-art results. Our approach provides better single model performance than the winning method of the last ImageNet challenge while being conceptually much simpler. Finally, we show that by increasing the temporal stride we can dramatically increase the tracker speed.
Overview
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.
Integrity signals
Registry
Cites
Statistics
Verification
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?
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Our identity preservation performance is only surpassed by [47] and offline methods which benefit from the processing of entire sequences at once.”
From TrackFormer: Multi-Object Tracking with Transformers (2022) · cites this paper as [47]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
Self-citationImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
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 73,472
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
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
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
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
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
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Research works printed but not linked: 1
- yang, 2016, ILSVRC2016 object detection from video Team NUIST
Cited by
Sorted by citations
Items 301–400 of 589
Video Text Detection by Attentive Spatiotemporal Fusion of Deep Convolutional Features
Wang, L., Shi, J., Wang, Y., & Su, F. (2019) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3343031.3350868 · Cited by 11
Multi-Target Tracking with Trajectory Prediction and Re-Identification
Li, X., Liu, Y., Wang, K., Yan, Y., & Wang, F. (2019) · Chinese Automation Congress · doi:10.1109/cac48633.2019.8996811 · Cited by 11
A Robust Deep Affinity Network for Multiple Ship Tracking
Zhang, W., He, X., Li, W., Zhang, Z., Luo, Y., Su, L. P., & Wang, P. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3077679 · Cited by 11
Zhang, T. T., Jin, P. J., Ge, Y., Moghe, R., & Jiang, X. (2022) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/03611981211068365 · Cited by 11
Fang, H., Li, X., Li, M., Wu, Y., & Zheng, W. (2022) · Electronics · doi:10.3390/electronics11244176 · Cited by 11
Forward Propagation, Backward Regression, and Pose Association for Hand Tracking in the Wild
Huang, M., Narasimhaswamy, S., Vazir, S., Ling, H., & Hoai, M. (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/cvpr52688.2022.00630 · Cited by 11
Object Detection in Video with Spatial-temporal Context AggregationPreprint
Luo, H., Huang, L., Shen, H., Li, Y., Huang, C., & Wang, X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.04988 · Cited by 11
SMOT: Single-Shot Multi Object TrackingPreprint
Li, W., Xiong, Y., Yang, S., Deng, S., & Xia, W. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.16031 · Cited by 13
Kyrkou, C. (2021) · Journal of Real-Time Image Processing · doi:10.1007/s11554-021-01077-z · Cited by 10
A feature temporal attention based interleaved network for fast video object detection
Yang, Y., Song, H., Sun, S., Chen, Y., Tang, X., & Shi, Q. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03309-3 · Cited by 10
Airport small object detection based on feature enhancement
Zhu, X., Liang, B., Fu, D., Huang, G., Yang, F., & Li, W. (2021) · IET Image Processing · doi:10.1049/ipr2.12387 · Cited by 10
Vision-Based HAR in UAV Videos Using Histograms and Deep Learning Techniques
Gundu, S., & Syed, H. (2023) · Sensors · doi:10.3390/s23052569 · Cited by 10
Joint Detection and Association for End-to-End Multi-object Tracking
Li, Y., Luo, X., Shi, J., Wang, X., Yin, G., & Wang, Z. (2023) · Neural Processing Letters · doi:10.1007/s11063-023-11397-9 · Cited by 10
CAMOT: Camera Angle-aware Multi-Object Tracking
Limanta, F., Uto, K., & Shinoda, K. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00635 · Cited by 10
Memory Warps for Learning Long-Term Online Video RepresentationsPreprint
Vu, T.-H., Choi, W., Schulter, S., & Chandraker, M. K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.10861 · Cited by 10
MTMMC: A Large-Scale Real-World Multi-Modal Camera Tracking Benchmark
Woo, S., Park, K., Shin, I., Kim, M., & Kweon, I. S. (2024) · 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/cvpr52733.2024.02108 · Cited by 12
PKF: Probabilistic Data Association Kalman Filter for Multi-Object Tracking
Cao, H., Pappas, G. J., & Atanasov, N. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3608646 · Cited by 9
YOLBO: You Only Look Back Once–A Low Latency Object Tracker Based on YOLO and Optical Flow
Kaputa, D. S., & Landy, B. P. (2021) · IEEE Access · doi:10.1109/access.2021.3080136 · Cited by 9
SSAT: Self-Supervised Associating Network for Multiobject Tracking
Chung, T.-Y., Cho, M., Lee, H.-S., & Lee, S. (2022) · IEEE transactions on circuits and systems for video technology (Print) · doi:10.1109/tcsvt.2022.3186751 · Cited by 10
High-Accuracy and Low-Latency Tracker for UAVs Monitoring Tibetan Antelopes
Luo, W., Li, X., Zhang, G., Shao, Q., Zhao, Y., Li, D., Zhao, Y., Li, X., Zhao, Z., Liu, Y., et al. (2023) · Remote Sensing · doi:10.3390/rs15020417 · Cited by 9
People Tracking in Video Surveillance Systems Based on Artificial Intelligence
Nasry, A., Ez-Zahout, A., & Omary, F. (2023) · Journal of Automation Mobile Robotics & Intelligent Systems · doi:10.14313/jamris/1-2023/8 · Cited by 9
Multi-Target Tracking Based on High-Order Appearance Feature Fusion
Han, G., Gao, Y., & Sun, N. (2019) · IEEE Access · doi:10.1109/access.2019.2955809 · Cited by 9
Video Object Detection Using Object's Motion Context and Spatio-Temporal Feature Aggregation
Kim, J., Koh, J., Lee, B., Yang, S., & Choi, J. W. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412715 · Cited by 9
Temporal Point Cloud Fusion With Scene Flow for Robust 3D Object Tracking
Yang, Y., Jiang, K., Yang, D., Jiang, Y., & Lu, X. (2022) · IEEE Signal Processing Letters · doi:10.1109/lsp.2022.3185948 · Cited by 9
Improving Multiple Pedestrian Tracking in Crowded Scenes with Hierarchical Association
Xiao, C., & Luo, Z. (2023) · Entropy · doi:10.3390/e25020380 · Cited by 9
Quasi-Dense Instance Similarity LearningPreprint
Pang, J., Qiu, L., Chen, H., Li, Q., Darrell, T., & Yu, F. (2020) · arXiv (Cornell University) · Cited by 9
Recurrent Neural Networks for video object detectionPreprint
Qasim, A. B., & Pettirsch, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.15740 · Cited by 9
Object Tracking by Detection with Visual and Motion CuesPreprint
Salscheider, N. O. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.07549 · Cited by 12
PatchNet -- Short-range Template Matching for Efficient Video ProcessingPreprint
Mao, H., Zhu, S., Han, S., & Dally, W. J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.07371 · Cited by 9
MoDist: Motion Distillation for Self-supervised Video Representation Learning.Preprint
Xiao, F., Tighe, J., & Modolo, D. (2021) · arXiv (Cornell University) · Cited by 9
JDECMC: Improving JDE based multi-object tracking with Camera Motion Compensation
Sinishaw, M. L., & Liu, S. (2024) · Displays · doi:10.1016/j.displa.2024.102682 · Cited by 8
DAFA: Diversity-Aware Feature Aggregation for Attention-Based Video Object Detection
Roh, S.-D., & Chung, K. (2022) · IEEE Access · doi:10.1109/access.2022.3203399 · Cited by 8
Robust appearance modeling for object detection and tracking: a survey of deep learning approaches
Mumuni, A., & Mumuni, F. (2022) · Progress in Artificial Intelligence · doi:10.1007/s13748-022-00290-6 · Cited by 8
Multiobject Tracking via Discriminative Embeddings for the Internet of Things
Li, H., Liang, X., Yin, H., Xu, L., Kong, X., & Gulliver, T. A. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3242739 · Cited by 8
An Efficient Adjacent Frame Fusion Mechanism for Airborne Visual Object Detection
Ye, Z., Peng, Y., Liu, W., Yin, W., Hao, H., Han, B., Zhu, Y., & Xiao, D. (2024) · Drones · doi:10.3390/drones8040144 · Cited by 8
G2EMOT: Guided Embedding Enhancement for Multiple Object Tracking in Complex Scenes
Ma, J., Wu, F., Li, C., Tang, C., Zhang, J., Xu, Z., Li, M., & Liu, D. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3449952 · Cited by 8
Joint Anchor-Feature Refinement for Real-Time Accurate Object Detection in Images and Videos
Yu, J., Chen, X., & Kong, S. (2021) · CRC Press eBooks · doi:10.1201/9781003144281-4 · Cited by 8
TagAttention: Mobile Object Tracing With Zero Appearance Knowledge by Vision-RFID Fusion
Shi, X., Cai, H., Wang, M., Wang, G., Huang, B., Xie, J., & Qian, C. (2021) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2021.3052805 · Cited by 8
Attention Modulated Multiple Object Tracking with Motion Enhancement and Dual Correlation
Wang, Y., Zhang, Z., Zhang, N., & Zeng, D. (2021) · Symmetry · doi:10.3390/sym13020266 · Cited by 8
Video representation learning through prediction for online object detection
Fujitake, M., & Sugimoto, A. (2022) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw54805.2022.00059 · Cited by 8
A novel robotic visual perception framework for underwater operation
Lu, Y., Chen, X., Wu, Z., Yu, J., & Wen, L. (2022) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.2100366 · Cited by 8
Attention-Guided Disentangled Feature Aggregation for Video Object Detection
Muralidhara, S., Hashmi, K. A., Pagani, A., Liwicki, M., Stricker, D., & Afzal, M. Z. (2022) · Sensors · doi:10.3390/s22218583 · Cited by 8
Geometry-Aware Video Object Detection for Static CamerasPreprint
Xu, D., Xie, W., & Zisserman, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.03140 · Cited by 8
Self-citationSPIN: A High Speed, High Resolution Vision Dataset for Tracking and Action Recognition in Ping PongPreprint
Schwarcz, S. L., Xu, P., D’Ambrosio, D. B., Kangaspunta, J., Angelova, A., Phan, H., & Jaitly, N. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.06640 · Cited by 9
Temporal Early Exits for Efficient Video Object DetectionPreprint
Sabet, A., Hare, J. S., Al‐Hashimi, B. M., & Merrett, G. V. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4001359 · Cited by 8
Meng, C., Sun, Z., Huo, L., Li, T., Chang, S., & Zhang, Z. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.129196 · Cited by 7
Ptak, B., Midtiby, H. S., & Kraft, M. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130503 · Cited by 7
Detection-by-tracking of traffic signs in videos
Zhang, Y., Wang, Z., Song, R., Yan, C., & Qi, Y. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02838-w · Cited by 7
An effective automatic object detection algorithm for continuous sonar image sequences
Shi, P., Sun, H., Fan, X., He, Q., Zhou, X., & Lü, L. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15837-5 · Cited by 7
Underwater Communication Using Full-Body Gestures and Optimal Variable-Length Prefix Codes
Koreitem, K., Li, J., Karp, I., Manderson, T., & Dudek, G. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793644 · Cited by 7
Joint Human Pose Estimation and Stereo 3D Localization
Deng, W., Bertoni, L., Kreiss, S., & Alahi, A. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197069 · Cited by 7
Integration of regularized l1 tracking and instance segmentation for video object tracking
Gürkan, F., & Günsel, B. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.09.072 · Cited by 7
Temporal Feature Enhancement Network with External Memory for Object Detection in Surveillance Video
Fujitake, M., & Sugimoto, A. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412056 · Cited by 7
RoI Feature Propagation for Video Object Detection
Cores, D., Mucientes, M., & Brea, V. M. (2020) · Frontiers in artificial intelligence and applications · doi:10.3233/faia200406 · Cited by 7
Bridging Images and Videos: A Simple Learning Framework for Large Vocabulary Video Object Detection
Woo, S., Park, K., Oh, S. W., Kweon, I. S., & Lee, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19806-9_14 · Cited by 7
Minimum Delay Object Detection From Video
Lao, D., & Sundaramoorthi, G. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00520 · Cited by 7
SCNet: Scale-aware coupling-structure network for efficient video object detection
Wang, F., Xu, Z., Gan, Y., Vong, C., & Liu, Q. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.03.110 · Cited by 7
Object Detection in Videos by Short and Long Range Object LinkingPreprint
Tang, P., Wang, C., Wang, X., Liu, W., Zeng, W., & Wang, J. (2018) · arXiv (Cornell University) · Cited by 7
Unsupervised Learning for Large-Scale Fiber Detection and Tracking in Microscopic Material ImagesPreprint
Yu, H., Guo, D., Yan, Z., Liu, W., Simmons, J. P., Przybyla, C., & Wang, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.10256 · Cited by 7
Deep Similarity Metric Learning for Real-Time Pedestrian TrackingPreprint
Thoreau, M., & Kottege, N. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.07592 · Cited by 7
Alzubi, T. M., & Mukhtar, U. R. (2025) · IEEE Access · doi:10.1109/access.2025.3551672 · Cited by 6
Multi-object tracking in surveillance scenarios based on joint detection and multi-feature fusion
Zhao, R., Gong, J., & Wang, Z. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07381-w · Cited by 6
Edwards, A., Huynh, D. Q., Gillham, B., Wang, J., & Sercombe, T. B. (2026) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-026-02869-5 · Cited by 6
Wu, P., Su, S., Zuo, Z., Bei, S., Wei, J., Guo, R., Tong, X., Zhang, J., & Huang, H. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3276357 · Cited by 6
Similarity- and Quality-Guided Relation Learning for Joint Detection and Tracking
Feng, W., Bai, L., Yao, Y., Gan, W., Wu, W., & Ouyang, W. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3279670 · Cited by 6
Feature Disentanglement Network: Multi-Object Tracking Needs More Differentiated Features
Guo, W. Q., Quan, W., Gao, J., Zhang, T., & Xu, C. (2023) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3626825 · Cited by 6
Tracking Aided Drone Bird Classification Using YOLO and LSTM
Kataria, M., & Lall, B. (2023) · IEEE International Conference on Image Processing Workshops (ICIPW) · doi:10.1109/icipc59416.2023.10328340 · Cited by 6
Localizing Active Objects from Egocentric Vision with Symbolic World Knowledge
Wu, T.-L., Zhou, Y., & Peng, N. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.304 · Cited by 6
Prasannakumar, A., & Mishra, D. (2024) · Journal of Imaging · doi:10.3390/jimaging10070171 · Cited by 6
Chen, H., Huang, W. X., & Zhang, T. (2025) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-025-00355-x · Cited by 6
A Novel Spatial and Temporal Context-Aware Approach for Drone-Based Video Object Detection
Pi, Z., Lian, Y., Chen, X., Wu, Y., Li, Y., & Jiao, L. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00027 · Cited by 6
Intelligent Vision with TensorFlow using Neural Network Algorithms
Visalatchi, A., Navasri, T., Ranjanipriya, P., & Yogamathi, R. (2020) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc48092.2020.iccmc-000175 · Cited by 6
de Langis, K., Fulton, M., & Sattar, J. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636099 · Cited by 6
The dramatically changing face of computer vision
Davies, E. R. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-822109-9.00010-2 · Cited by 6
Dense Pixel-Level Interpretation of Dynamic Scenes With Video Panoptic Segmentation
Kim, D., Woo, S., Lee, J., & Kweon, I. S. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3183440 · Cited by 6
Real-Time Robust Video Object Detection System Against Physical-World Adversarial Attacks
Han, H., Hu, X., Hao, Y., Xu, K., Dang, P., Wang, Y., Zhao, Y., Du, Z., Guo, Q., Wang, Y., et al. (2023) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2023.3305932 · Cited by 6
Hajizadeh, M., Sabokrou, M., & Rahmani, A. T. (2024) · Machine Vision and Applications · doi:10.1007/s00138-023-01504-0 · Cited by 6
A Review of Detection-Related Multiple Object Tracking in Recent Times
Li, S., Cao, Y. S., & Xie, X. (2024) · International Conference on Advanced Communications Technology (ICACT) · doi:10.23919/icact60172.2024.10471984 · Cited by 6
DAF-Net: dense attention feature pyramid network for multiscale object detection
Achinek, D. N., Shehu, I. S., Athuman, A. M., & Fu, X. (2024) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-024-00323-x · Cited by 6
Learning Multi-frame Visual Representation for Joint Detection and Tracking of Small Objects.
Yoshihashi, R., Trinh, T. T., Kawakami, R., You, S., Iida, M., & Naemura, T. (2017) · Cited by 6
Multiple-Joint Pedestrian Tracking Using Periodic Models
Dolatabadi, M., Elfring, J., & van de Molengraft, R. (2020) · Sensors · doi:10.3390/s20236917 · Cited by 6
Survey of Livestock Counting and Tracking Methods
Sarwar, F., Pasang, T., Griffin, A., & Saeed-Ur-Rehman (2020) · Nusantara Science and Technology Proceedings · doi:10.11594/nstp.2020.0523 · Cited by 6
Shyam, P., Mishra, S., Yoon, K., & Kim, K.-S. (2022) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv51971.2022.9827395 · Cited by 6
Introduce GIoU into RFB net to optimize object detection bounding box
Li, X., Pu, H., Wei, Y., JiangChuan, L., & Xu, H. (2019) · International Conference on Communication and Information Processing (ICCIP) · doi:10.1145/3369985.3370011 · Cited by 6
EDF: Ensemble, Distill, and Fuse for Easy Video LabelingPreprint
Zhou, G., Dulloor, S. R., Andersen, D. G., & Kaminsky, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.03626 · Cited by 6
Enhancing Real-Time Object Detection With Optical Flow-Guided Streaming Perception
Wang, T., Zhu, L., & Huang, H. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3525796 · Cited by 5
Beyond Boxes: Mask-Guided Spatio-Temporal Feature Aggregation for Video Object Detection
Hashmi, K. A., Sheikh, T. U., Stricker, D., & Afzal, M. Z. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00788 · Cited by 5
SORT-LFR: Revisiting SORT for Multi-Object Tracking in Low-Frame-Rate Videos
Zeng, L. W., Huang, Y., Lin, Y. J., Zhu, Z., Zhang, X., Liu, Y. M., Li, Y., & Zheng, Y. (2026) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2026.3651130 · Cited by 5
SPAMming Labels: Efficient Annotations for the Trackers of Tomorrow
Cetintas, O., Meinhardt, T., Brasó, G., & Leal-Taixé, L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73254-6_22 · Cited by 5
Sang, H., & Suo, P. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-03837-x · Cited by 5
Huang, J., He, T., Xu, J., Wu, W., & Wu, W. (2025) · IEEE Access · doi:10.1109/access.2025.3561769 · Cited by 5
Improving Industrial Safety Gear Detection through Re-ID conditioned Detector
Ravikiran, M., & Sen, S. (2019) · IEEE Applied Imagery Pattern Recognition Workshop · doi:10.1109/aipr47015.2019.9174597 · Cited by 5
Multiple Object Tracking using YOLO-based Detector
Lin, S. D., Chang, T., & Chen, W. (2021) · Journal of Imaging Science and Technology · doi:10.2352/j.imagingsci.technol.2021.65.4.040401 · Cited by 5
Multi-object tracking with deep learning ensemble for unmanned aerial system applications
Xie, W., Ide, J. S., Izadi, D., Banger, S., Walker, T. T., Ceresani, R., Spagnuolo, D. S., Guagliano, C., Díaz, H., & Twedt, J. C. (2021) · Artificial Intelligence and Machine Learning in Defense Applications · doi:10.1117/12.2600209 · Cited by 5
DIOR: DIstill Observations to Representations for Multi-Object Tracking and Segmentation
Cai, J., Wang, Y., Hsu, H.-M., Zhang, H., & Hwang, J. (2022) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw54805.2022.00058 · Cited by 5
Learning Correlation for Online Multiple Object Tracking
Wang, Y., Zhuang, C., Ye, H., Yan, Y., & Wang, H. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746986 · Cited by 5
Fast Vehicle Detection and Tracking on Fisheye Traffic Monitoring Video using Motion Trail
Ardianto, S., Hang, H., & Cheng, W.-H. (2023) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas46773.2023.10181767 · Cited by 5
Self-supervised spatial–temporal feature enhancement for one-shot video object detection
Yao, X., & Yang, X. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128219 · Cited by 5
Zhou, K., Wang, Y., Lv, T., Shen, Q., & Cao, X. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3449994 · Cited by 5
Multi-Information-Assisted Joint Detection and Tracking of Ground Moving Target for Airborne Radar
Liu, R., Li, X., Sun, J., & Shan, T. (2025) · Remote Sensing · doi:10.3390/rs17122093 · Cited by 5
Metadata
- Authors
- Feichtenhofer, C., Pinz, A., & Zisserman, A.
- Affiliations
- Austrian Academy of Sciences (AT)
- VRVis GmbH (Austria) (AT)
- University of Oxford (GB)
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 3057-3065
- OpenAlex
- W2756784878
- Cited by
- 595
- References
- 44
- Citations by year
- 2026: 8 · 2025: 36 · 2024: 53 · 2023: 79 · 2022: 84 · 2021: 119 · 2020: 100 · 2019: 76 · 2018: 35 · 2017: 5
- FWCI
- 17.00
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Austrian Science Fund (P 27076)
- Engineering and Physical Sciences Research Council (EP/M013774/1, EP/M013774/)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- video object tracking, object detection, joint detection and tracking, multi-task learning, correlation features, object co-occurrence, tracklets
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
