The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Multiple Hypothesis Tracking Revisited
Abstract
This paper revisits the classical multiple hypotheses tracking (MHT) algorithm in a tracking-by-detection framework. The success of MHT largely depends on the ability to maintain a small list of potential hypotheses, which can be facilitated with the accurate object detectors that are currently available. We demonstrate that a classical MHT implementation from the 90's can come surprisingly close to the performance of state-of-the-art methods on standard benchmark datasets. In order to further utilize the strength of MHT in exploiting higher-order information, we introduce a method for training online appearance models for each track hypothesis. We show that appearance models can be learned efficiently via a regularized least squares framework, requiring only a few extra operations for each hypothesis branch. We obtain state-of-the-art results on popular tracking-by-detection datasets such as PETS and the recent MOT challenge.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“These techniques, widely adopted in the tracking community, enable the storage of various masks as memory at each time step and delay the association decision to obtain video-level optimal result.”
From SAM2LONG: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory Tree (2025) · cites this paper as [32]
In the citing paper
“Among these, Multiple Hypothesis Tracking (MHT) [15,32,51] stands out as a robust paradigm that maintains tree search to construct and manage memory.”
From SAM2LONG: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory Tree (2025) · cites this paper as [32]
Citations
Cites
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
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
Design and Analysis of Modern Tracking Systems
Blackman, S. S., & Populi, R. (1999) · Cited by 3,509
An algorithm for tracking multiple targets
Reid, D. (1979) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1979.1102177 · Cited by 3,059
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
Visual Tracking: An Experimental Survey
Smeulders, A. W. M., Chu, D. M., Cucchiara, R., Calderara, S., Dehghan, A., & Shah, M. A. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.230 · Cited by 1,527
MOTChallenge 2015: Towards a Benchmark for Multi-Target TrackingPreprint
Leal-Taixé, L., Milan, A., Reid, I. D., Roth, S., & Konrad Schindler, B. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.01942 · Cited by 1,034
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
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
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
Research works printed but not linked: 2
- ben, 2014, Multicommodity network flow for tracking multiple people, PAMI, 2
- wang, 2015, Learning optimal parameters for multitarget tracking, BMVC, 7
Cited by
Sorted by citations
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
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
HOTA: A Higher Order Metric for Evaluating Multi-object Tracking
Luiten, J., Os̆ep, A., Dendorfer, P., Torr, P., Geiger, A., Leal-Taixé, L., & Leibe, B. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-020-01375-2 · Cited by 1,430
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
Multiple object tracking: A literature review
Luo, W., Xing, J., Milan, A., Zhang, X., Liu, W., & Kim, T. (2020) · Artificial Intelligence · doi:10.1016/j.artint.2020.103448 · Cited by 1,271
Metadata
- Authors
- Kim, C., Li, F., Ciptadi, A., & Rehg, J. M.
- Affiliations
- Georgia Institute of Technology (US)
- Oregon State University (US)
- Venue
- 2015 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 4696-4704
- OpenAlex
- W2237765446
- Cited by
- 666
- References
- 46
- Citations by year
- 2026: 9 · 2025: 24 · 2024: 44 · 2023: 66 · 2022: 84 · 2021: 96 · 2020: 103 · 2019: 93 · 2018: 76 · 2017: 45 · 2016: 24 · 2015: 2
- FWCI
- 20.10
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- 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 › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
- Keywords
- multiple hypothesis tracking, tracking-by-detection, object tracking, regularized least squares, higher-order information
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
