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
MCMC-based particle filtering for tracking a variable number of interacting targets
Khan, Z., Balch, T. R., & Dellaert, F. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.223 · Cited by 776
Cox, I. J., & Hingorani, S. L. (1996) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.481539 · Cited by 665
Online Multiperson Tracking-by-Detection from a Single, Uncalibrated Camera
Breitenstein, M. D., Reichlin, F., Leibe, B., Koller-Meier, E., & Gool, L. V. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.232 · Cited by 581
Continuous Energy Minimization for Multitarget Tracking
Milan, A., Roth, S., & Schindler, K. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.103 · Cited by 556
Video Segmentation by Tracking Many Figure-Ground Segments
Li, F., Kim, T., Humayun, A., Tsai, D., & Rehg, J. M. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.273 · Cited by 523
Self-citationBae, S., & Yoon, K. (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.2014.159 · Cited by 417
3D Traffic Scene Understanding From Movable Platforms
Geiger, A., Lauer, M., Wojek, C., Stiller, C., & Urtasun, R. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.185 · Cited by 397
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
Discrete-continuous optimization for multi-target tracking
Andriyenko, A., Konrad Schindler, B., & Roth, S. (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.2012.6247893 · Cited by 341
Markov Chain Monte Carlo Data Association for Multi-Target Tracking
Oh, S., Russell, S., & Sastry, S. (2009) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2009.2012975 · Cited by 337
The Way They Move: Tracking Multiple Targets with Similar Appearance
Dicle, C., Camps, O., & Sznaier, M. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.286 · Cited by 316
An online learned CRF model for multi-target tracking
Yang, B., & Nevatia, R. (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.2012.6247907 · Cited by 295
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
Multi-target tracking by on-line learned discriminative appearance models
Kuo, C.-H., Huang, C., & Nevatia, R. (2010) · 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.2010.5540148 · Cited by 279
Multi-target Tracking by Lagrangian Relaxation to Min-cost Network Flow
Butt, A. A., & Collins, R. T. (2013) · 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.2013.241 · Cited by 271
Multiobject tracking as maximum weight independent set
Brendel, W., Amer, M., & Todorović, S. (2011) · doi:10.1109/cvpr.2011.5995395 · Cited by 269
Tracking multiple people under global appearance constraints
Shitrit, H. B., Berclaz, J., Fleuret, F., & Fua, P. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126235 · Cited by 227
Bayesian Multi-object Tracking Using Motion Context from Multiple Objects
Yoon, J. H., Yang, M., Lim, J., & Yoon, K. (2015) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2015.12 · Cited by 216
How does person identity recognition help multi-person tracking?
Kuo, C.-H., & Nevatia, R. (2011) · doi:10.1109/cvpr.2011.5995384 · Cited by 207
Detection- and Trajectory-Level Exclusion in Multiple Object Tracking
Milan, A., Schindler, K., & Roth, S. (2013) · 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.2013.472 · Cited by 196
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
Multi-Commodity Network Flow for Tracking Multiple People
Shitrit, H. B., Berclaz, J., Fleuret, F., & Fua, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.210 · Cited by 172
A new algorithm for the maximum-weight clique problem
Östergård, P. R. J. (2001) · doi:10.5555/766502.766504 · Cited by 154
PETS2010: Dataset and Challenge
Ferryman, J. M., & Ellis, A. (2010) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2010.90 · Cited by 121
A new trust region technique for the maximum weight clique problem
Busygin, S. (2006) · Discrete Applied Mathematics · doi:10.1016/j.dam.2005.04.010 · Cited by 119
Multitarget data association with higher-order motion models
Collins, R. T. (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.2012.6247870 · Cited by 118
An algorithm for multiple object trajectory tracking
Han, M., Xu, W. D., Hai, T., & Gong, Y. (2004) · 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.2004.1315122 · Cited by 114
Tracking Sports Players with Context-Conditioned Motion Models
Liu, J., Carr, P., Collins, R. T., & Liu, Y. (2013) · 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.2013.239 · Cited by 112
Challenges of Ground Truth Evaluation of Multi-target Tracking
Milan, A., Schindler, K., & Roth, S. (2013) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2013.111 · Cited by 96
Multiple Target Tracking by Learning-Based Hierarchical Association of Detection Responses
Huang, C., Li, Y., & Nevatia, R. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.159 · Cited by 83
Vision-Based Multiple Interacting Targets Tracking via On-Line Supervised Learning
Song, X., Cui, J., Zha, H., & Zhao, H. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88690-7_48 · Cited by 77
Enhancing Linear Programming with Motion Modeling for Multi-target Tracking
McLaughlin, N., del Rincón, J. M., & Miller, P. C. (2015) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2015.17 · Cited by 54
The Maximum Weight Independent Set Problem for Data Association in Multiple Hypothesis Tracking
Papageorgiou, D. J., & Salpukas, M. R. (2008) · Lecture notes in control and information sciences · doi:10.1007/978-3-540-88063-9_15 · Cited by 49
A New Algorithm for the Maximum-Weight Clique Problem
Östergård, P. R. J. (2000) · Electronic Notes in Discrete Mathematics · doi:10.1016/s1571-0653(05)00757-2 · Cited by 48
Latent Data Association: Bayesian Model Selection for Multi-target Tracking
Segal, A. V., & Reid, I. D. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.361 · Cited by 46
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
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
