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Globally-optimal greedy algorithms for tracking a variable number of objects
Abstract
We analyze the computational problem of multi-object tracking in video sequences. We formulate the problem using a cost function that requires estimating the number of tracks, as well as their birth and death states. We show that the global solution can be obtained with a greedy algorithm that sequentially instantiates tracks using shortest path computations on a flow network. Greedy algorithms allow one to embed pre-processing steps, such as nonmax suppression, within the tracking algorithm. Furthermore, we give a near-optimal algorithm based on dynamic programming which runs in time linear in the number of objects and linear in the sequence length. Our algorithms are fast, simple, and scalable, allowing us to process dense input data. This results in state-of-the-art performance.
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He, J., Huang, Z., Wang, N., & Zhang, Z. (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/cvpr46437.2021.00526 · Cited by 153
Virtual KITTI 2Preprint
Cabon, Y., Murray, N., & Humenberger, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.10773 · Cited by 153
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
Multi-class Multi-object Tracking Using Changing Point Detection
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Wen, L., Du, D., Zhu, P., Hu, Q., Wang, Q., Bo, L., & Lyu, S. (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/cvpr46437.2021.00772 · Cited by 150
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Lee, K.-H., & Hwang, J. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2455418 · Cited by 139
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Visual Semantic Search: Retrieving Videos via Complex Textual Queries
Lin, D., Fidler, S., Kong, C., & Urtasun, R. (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.2014.340 · Cited by 138
Metadata
- Authors
- Pirsiavash, H., Ramanan, D., & Fowlkes, C. C.
- Affiliations
- University of California, Irvine (US)
- Venue
- CVPR 2011
- Publisher
- IEEE
- Published
- 2011-06
- Pages
- 1201-1208
- OpenAlex
- W2016135469
- Cited by
- 790
- References
- 26
- Citations by year
- 2026: 4 · 2025: 14 · 2024: 17 · 2023: 37 · 2022: 39 · 2021: 59 · 2020: 73 · 2019: 87 · 2018: 95 · 2017: 79 · 2016: 90 · 2015: 81 · 2014: 48 · 2013: 40 · 2012: 22
- FWCI
- 27.44
- Type
- conference paper
- Language
- en
- Open access
- closed
- Funders
- Office of Naval Research
- 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 Vision and Imaging
- Keywords
- multi-target tracking, video sequences, greedy algorithm, track initialization, flow networks, shortest path, dynamic programming, non-maximum suppression, computational complexity
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.
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