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Robust People Tracking with Global Trajectory Optimization
Open accessAbstract
Given three or four synchronized videos taken at eye level and from different angles, we show that we can effectively use dynamic programming to accurately follow up to six individuals across thousands of frames in spite of significant occlusions. In addition, we also derive metrically accurate trajectories for each one of them. Our main contribution is to show that multi-person tracking can be reliably achieved by processing individual trajectories separately over long sequences, provided that a reasonable heuristic is used to rank these individuals and avoid confusing them with one another. In this way, we achieve robustness by finding optimal trajectories over many frames while avoiding the combinatorial explosion that would result from simultaneously dealing with all the individuals.
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Cited by
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
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
Learning to associate: HybridBoosted multi-target tracker for crowded scene
Li, Y., Huang, C., & Nevatia, R. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206735 · Cited by 633
Learning a Neural Solver for Multiple Object Tracking
Braso, G., & Leal-Taixe, L. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00628 · Cited by 504
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
A Linear Programming Approach for Multiple Object Tracking
Jiang, H., Fels, S., & Little, J. J. (2007) · 2007 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2007.383180 · Cited by 316
Learning 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
Object Detection and Tracking for Autonomous Navigation in Dynamic Environments
Ess, A., Schindler, K., Leibe, B., & Van Gool, L. (2010) · The International Journal of Robotics Research · doi:10.1177/0278364910365417 · Cited by 191
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
Metadata
- Authors
- Berclaz, J., Fleuret, F., & Fua, P.
- Venue
- 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06)
- Publisher
- IEEE
- Published
- 2006-07-10
- Volume
- 1
- Pages
- 744-750
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2096846396
- Cited by
- 201
- References
- 0
- FWCI
- 11.88
- Type
- conference paper
- Language
- en
- Open access
- green
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