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.
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“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]
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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
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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
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