Learning a Neural Solver for Multiple Object Tracking
Open accessAbstract
Graphs offer a natural way to formulate Multiple Object Tracking (MOT) within the tracking-by-detection paradigm. However, they also introduce a major challenge for learning methods, as defining a model that can operate on such structured domain is not trivial. As a consequence, most learning-based work has been devoted to learning better features for MOT and then using these with well-established optimization frameworks. In this work, we exploit the classical network flow formulation of MOT to define a fully differentiable framework based on Message Passing Networks (MPNs). By operating directly on the graph domain, our method can reason globally over an entire set of detections and predict final solutions. Hence, we show that learning in MOT does not need to be restricted to feature extraction, but it can also be applied to the data association step. We show a significant improvement in both MOTA and IDF1 on three publicly available benchmarks. Our code is available at https://bit.ly/motsolv.
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
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.
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
ImageNet 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 · see citing works
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 · see citing works
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · 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
Semi-supervised classification with graph convolutional networks
Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853 · 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
The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2005605 · Cited by 9,582 · see citing works
Network Flows: Theory, Algorithms, and Applications.
Smith, D. K., Ahuja, R. K., Magnanti, T. L., & Orlin, J. B. (1994) · Journal of the Operational Research Society · doi:10.2307/2583863 · Cited by 8,121 · see citing works
Neural Message Passing for Quantum ChemistryPreprint
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., & Dahl, G. E. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.01212 · Cited by 5,414 · see citing works
Advances In Deep Learning On Graphs (Gsp'18 Workshop)
Defferrard, M. (2016) · Figshare · doi:10.5281/zenodo.1286817 · Cited by 4,977 · 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
Spectral Networks and Locally Connected Networks on Graphs
Bruna, J., Zaremba, W., Szlam, A., & LeCun, Y. (2013) · arXiv · doi:10.48550/arxiv.1312.6203 · Cited by 3,983 · 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
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
You'll never walk alone: Modeling social behavior for multi-target tracking
Pellegrini, S., Ess, A., Schindler, K., & van Gool, L. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459260 · Cited by 1,683 · cited 1× · see citing works
“Data association can be done on a frame-by-frame basis for online applications [10, 20, 51] or track-by-track [7].”
BackgroundMOT16: 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
Tracking Without Bells and Whistles
Bergmann, P., Meinhardt, T., & Leal-Taixe, L. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00103 · Cited by 1,110 · see citing works
Self-citationDeepFlow: 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
Multiple Object Tracking Using K-Shortest Paths Optimization
Berclaz, J., Fleuret, F., Turetken, E., & Fua, P. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.21 · Cited by 1,015 · see citing works
Global data association for multi-object tracking using network flows
Zhang, L., Li, Y., & Nevatia, R. (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.2008.4587584 · Cited by 945 · 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
Multicamera People Tracking with a Probabilistic Occupancy Map
Fleuret, F., Berclaz, J., Lengagne, R., & Fua, P. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1174 · Cited by 823 · 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
A mobile vision system for robust multi-person tracking
Ess, A., Leibe, B., Schindler, K., & Van Gool, L. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587581 · Cited by 721 · 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
Segmentation of Moving Objects by Long Term Video Analysis
Ochs, P., Malik, J., & Brox, T. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.242 · Cited by 585 · see citing works
Kasturi, R., Goldgof, D., Soundararajan, P., Manohar, V., Garofolo, J. S., Bowers, R., Boonstra, M., Korzhova, V., & Zhang, J. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.57 · Cited by 580 · 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
Features for Multi-target Multi-camera Tracking and Re-identification
Ristani, E., & Tomasi, C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00632 · Cited by 556 · see citing works
Robust tracking-by-detection using a detector confidence particle filter
Breitenstein, M. D., Reichlin, F., Leibe, B., Koller-Meier, E., & Gool, L. V. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459278 · Cited by 555 · 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
Neural Relational Inference for Interacting Systems
Kipf, T., Fetaya, E., Wang, K.-C. J., Welling, M., & Zemel, R. S. (2018) · UvA-DARE (University of Amsterdam) · Cited by 475 · 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
Self-citationNear-Online Multi-target Tracking with Aggregated Local Flow Descriptor
Choi, W. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.347 · Cited by 409 · 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
GMCP-Tracker: Global Multi-object Tracking Using Generalized Minimum Clique Graphs
Zamir, A., Dehghan, A., & Shah, M. A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33709-3_25 · Cited by 360 · see citing works
A Unified Framework for Multi-target Tracking and Collective Activity Recognition
Choi, W., & Savarese, S. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33765-9_16 · Cited by 359 · see citing works
Combinatorial Optimization with Graph Convolutional Networks and Guided Tree SearchPreprint
Li, Z., Chen, Q., & Koltun, V. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.10659 · Cited by 351 · see citing works
Gao, J., Zhang, T., & Xu, C. (2019) · 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.2019.00478 · Cited by 343 · see citing works
A Linear Programming Approach for Multiple Object Tracking
Jiang, H., Fels, S., & Little, J. J. (2007) · 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.2007.383180 · Cited by 323 · see citing works
Learning an Image-Based Motion Context for Multiple People Tracking
Leal-Taixé, L., Fenzi, M., Kuznetsova, A., Rosenhahn, B., & Savarese, S. (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.2014.453 · Cited by 281 · see citing works
Spatial-Temporal Relation Networks for Multi-Object Tracking
Xu, J., Cao, Y., Zhang, Z., & Hu, H. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00409 · Cited by 275 · 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
Chu, P., & Ling, H. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00627 · Cited by 246 · 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
Deep Network Flow for Multi-object Tracking
Schulter, S., Vernaza, P., Choi, W., & Chandraker, M. K. (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.2017.292 · Cited by 213 · 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
Robust People Tracking with Global Trajectory Optimization
Berclaz, J., Fleuret, F., & Fua, P. (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.2006.258 · Cited by 202 · see citing works
Motion Segmentation & Multiple Object Tracking by Correlation Co-Clustering
Keuper, M., Tang, S., Andres, B., Brox, T., & Schiele, B. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2876253 · Cited by 201 · see citing works
Subgraph decomposition for multi-target tracking
Tang, S., Andres, B., Andriluka, M., & Schiele, B. (2015) · 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.2015.7299138 · Cited by 195 · see citing works
Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question AnsweringPreprint
Narasimhan, M., Lazebnik, S., & Schwing, A. G. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.00538 · Cited by 184 · see citing works
Joint tracking and segmentation of multiple targets
Milan, A., Leal-Taixé, L., Schindler, K., & Reid, I. D. (2015) · 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.2015.7299178 · Cited by 183 · see citing works
End-to-end Learning of Multi-sensor 3D Tracking by Detection
Frossard, D., & Urtasun, R. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8462884 · Cited by 135 · see citing works
Multiple Target Tracking Using Spatio-Temporal Markov Chain Monte Carlo Data Association
Yu, Q., Medioni, G., & Cohen, I. (2007) · 2007 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2007.382991 · Cited by 132 · 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
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
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
Iterative Multiple Hypothesis Tracking With Tracklet-Level Association
Sheng, H., Chen, J., Zhang, Y., Ke, W., Xiong, Z., & Jingyi Yu, A. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2881123 · Cited by 108 · see citing works
Neural Graph Matching Networks for Fewshot 3D Action Recognition
Guo, M., Chou, E., Huang, D.-A., Song, S., Yeung, S. Y., & Fei-Fei, L. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01246-5_40 · Cited by 107 · 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
Multiple People Tracking Using Body and Joint Detections
Henschel, R. D., Zou, Y., & Rosenhahn, B. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00105 · Cited by 96 · see citing works
Branch-and-price global optimization for multi-view multi-target tracking
Leal-Taixé, L., Pons‐Moll, G., & Rosenhahn, B. (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.2012.6247901 · Cited by 84 · see citing works
Yoon, Y.-C., Boragule, A., Song, Y.-M., Yoon, K., & Jeon, M. (2018) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2018.8639078 · Cited by 83 · see citing works
Efficient track linking methods for track graphs using network-flow and set-cover techniques
Wu, Z., Kunz, T., & Betke, M. (2011) · doi:10.1109/cvpr.2011.5995515 · Cited by 76 · see citing works
Aggregate Tracklet Appearance Features for Multi-Object Tracking
Chen, L., Ai, H., Chen, R., & Zhuang, Z. (2019) · IEEE Signal Processing Letters · doi:10.1109/lsp.2019.2940922 · Cited by 63 · 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
Improvements to Frank-Wolfe optimization for multi-detector multi-object tracking.Preprint
Henschel, R. D., Leal-Taixé, L., Cremers, D., et al. (2017) · arXiv (Cornell University) · Cited by 36 · see citing works
Learning Optimal Parameters For Multi-target Tracking
Wang, S., & Fowlkes, C. C. (2015) · British Machine Vision Conference (BMVC) · doi:10.5244/c.29.4 · Cited by 33 · see citing works
Spatial-Temporal Relation Networks for Multi-Object TrackingPreprint
Xu, J., Cao, Y., Zhang, Z., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.11489 · Cited by 27 · see citing works
Global data association for the Probability Hypothesis Density filter using network flows
Wojke, N., & Paulus, D. W. R. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487180 · Cited by 18 · see citing works
Tracking with multi-level featuresPreprint
Henschel, R. D., Leal-Taixé, L., Rosenhahn, B., & Schindler, K. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.07304 · Cited by 17 · see citing works
Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term DependenciesPreprint
Sadeghian, A., Alahi, A., & Savarese, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.01909 · Cited by 15 · see citing works
Ma, C., Yang, C., Yang, F., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.04555 · Cited by 13 · see citing works
Fusion of Head and Full-Body Detectors for Multi-Object TrackingPreprint
Henschel, R. D., Leal-Taixé, L., Cremers, D., & Rosenhahn, B. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.08314 · Cited by 7 · see citing works
Online Multi-Object Tracking with Instance-Aware Tracker and Dynamic Model RefreshmentPreprint
Chu, P., Fan, H., Tan, C. C., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.08231
Flagged first, then by citations
Research works printed but not linked: 12
- [3][3] R. Ahuja, T. Magnanti, and J. Orlin. Network flows: Theory, algorithms and applications . Prentice Hall, Upper Saddle River, NJ, USA, 1993. 4, 14 -
- [4][4] P. Battaglia, R. Pascanu, M. Lai, D. Jimenez Rezende, and k. kavukcuoglu. Interaction networks for learning about objects, relations and physics. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, editors, Advances in Neural Information Processing Systems 29 , pages 45024510. Curran Associates, Inc., 2016. 4 -
- [16][16] W. Choi. Near-online multi-target tracking with aggregated local flow descriptor. ICCV , 2015. 18 -
- [20][20] M. Defferrard, X. Bresson, and P. Vandergheynst. Convolutional neural networks on graphs with fast localized spectral filtering. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, editors, Advances in Neural Information Processing Systems 29 , pages 3844-3852. Curran Associates, Inc., 2016. 2 -
- [35][35] M. Keuper, S. Tang, B. Andres, T. Brox, and B. Schiele. Motion segmentation and multiple object tracking by correlation co-clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2019. 16, 17 -
- [36][36] C. Kim, F. Li, A. Ciptadi, and J. Rehg. Multiple hypothesis tracking revisited: Blending in modern appearance model. ICCV , 2015. 18 -
- [38][38] S. Kim, S. Kwak, J. Feyereisl, and B. Han. Online multitarget tracking by large margin structured learning. pages 98-111, 2013. 2 -
- [47][47] L. Leal-Taix´ e, A. Milan, I. Reid, S. Roth, and K. Schindler. Motchallenge 2015: Towards a benchmark for multi-target tracking. arXiv:1504.01942 , 2015. 17 - · arXiv
- [72][72] S. Tang, B. Andres, M. Andriluka, and B. Schiele. Multiperson tracking by multicuts and deep matching. In ECCV Workshop on Benchmarking Mutliple Object Tracking , 2016. 16 -
- [74][74] S. Tang, M. Andriluka, B. Andres, and B. Schiele. Multiple people tracking by lifted multicut and person reidentification. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pages 3701-3710, July 2017. 8, 17, 18 -
- [75][75] S. Tang, M. Andriluka, and B. Schiele. Multi people tracking with lifted multicut and person re-identification. CVPR , 2017. 1, 2 -
- [87][87] L. Zhang, Y. Li, and R. Nevatia. Global data association for multi-object tracking using network flows. CVPR , 2008. 1, 2, 3, 12 -
Cited by
Sorted by citations
FairMOT: On the Fairness of Detection and Re-identification in Multiple Object Tracking
Zhang, Y., Wang, C., Wang, X., Zeng, W., & Liu, W. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01513-4 · Cited by 1,861 · see citing works
Towards Real-Time Multi-Object Tracking
Wang, Z., Zheng, L., Liu, Y., Li, Y., & Wang, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58621-8_7 · Cited by 1,195 · see citing works
Liu, J., Fan, X., Huang, Z., Wu, G., Liu, R., Zhong, W., & Luo, Z. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00571 · Cited by 1,336 · see citing works
StrongSORT: Make DeepSORT Great Again
Du, Y., Zhao, Z., Song, Y., Zhao, Y., Su, F., Gong, T., & Meng, H. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3240881 · Cited by 1,191 · see citing works
Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking
Cao, J., Pang, J., Weng, X., Khirodkar, R., & Kitani, K. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00934 · Cited by 1,115 · see citing works
TrackFormer: Multi-Object Tracking with Transformers
Meinhardt, T., Kirillov, A., Leal-Taixe, L., & Feichtenhofer, C. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00864 · Cited by 1,081 · see citing works
“1 Furthermore, every MOT17 [13] frame is jittered by 1% with respect to the original image size similar to the adjacent frame simulation.”
MethodSelf-citationMOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking
Dendorfer, P., Os̆ep, A., Milan, A., Schindler, K., Cremers, D., Reid, I., Roth, S., & Leal-Taixé, L. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01393-0 · Cited by 374 · see citing works
Self-citationLearning to Track with Object Permanence
Tokmakov, P., Li, J., Burgard, W., & Gaidon, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01068 · Cited by 226 · see citing works
Improving Multiple Pedestrian Tracking by Track Management and Occlusion Handling
Stadler, D., & Beyerer, J. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01081 · Cited by 147 · see citing works
A Framework for Deprecating Datasets: Standardizing Documentation, Identification, and Communication
Luccioni, A. S., Corry, F., Sridharan, H., Ananny, M., Schultz, J., & Crawford, K. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533086 · Cited by 22 · see citing works
Vehicle Detection and Tracking from Surveillance Cameras in Urban Scenes
Messoussi, O., de Magalhães, F. G., Lamarre, F., Perreault, F., Sogoba, I., Bilodeau, G.-A., & Nicolescu, G. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-90436-4_15 · Cited by 9 · see citing works
- Authors
- Braso, G., & Leal-Taixe, L.
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 6246-6256
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2995136401
- Cited by
- 504
- References
- 78
- Citations by year
- 2026: 28 · 2025: 68 · 2024: 96 · 2023: 95 · 2022: 117 · 2021: 76 · 2020: 24
- FWCI
- 23.81
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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 › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- multiple object tracking, message passing networks, network flow formulation, data association, tracking-by-detection, graph neural networks, differentiable optimization
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
