Cited by 667Video Surveillance and Tracking MethodsAutonomous Vehicle Technology and SafetyTarget Tracking and Data Fusion in Sensor Networks

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Multiple Hypothesis Tracking Revisited

Kim, Chanho, Li, Fuxin, Ciptadi, Arridhana, Rehg, James M.·2015·2015 IEEE International Conference on Computer Vision (ICCV)

DOI 10.1109/iccv.2015.533

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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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Authors: Kim, C., Li, F., Ciptadi, A., & Rehg, J. M.
Affiliations: Georgia Institute of Technology (US)
Oregon State University (US)
ORCID: Kim, Chanho
Li, Fuxin
Ciptadi, Arridhana
Rehg, James M.
Venue: 2015 IEEE International Conference on Computer Vision (ICCV)
Publisher: IEEE
Published: 2015-12
Pages: 4696-4704
DOI: 10.1109/iccv.2015.533
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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