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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Multiple Hypothesis Tracking (MHT) techniques enable storage of various masks as memory at each time step and delay association decision to obtain video-level optimal result.
“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]
Multiple Hypothesis Tracking (MHT) stands out as a robust paradigm that maintains tree search to construct and manage memory.
“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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- 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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