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On the detection-to-track association for online multi-object tracking
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Lin, Xufeng, Li, Chang-Tsun, Sanchez Silva, Victor and Maple, Carsten (2021) On the detection-to-track association for online multi-object tracking. Pattern Recognition Letters, 146 . pp. 200-207. doi:10.1016/j.patrec.2021.03.022
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WRAP-On-the-detection-to-track-association-online-multi-object-tracking-2021.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only until 22 March 2022. Contact author directly, specifying your specific needs. - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (2362Kb) |
Official URL: https://doi.org/10.1016/j.patrec.2021.03.022
Abstract
Driven by recent advances in object detection with deep neural networks, the tracking-by-detection paradigm has gained increasing prevalence in the research community of multi-object tracking (MOT).
It has long been known that appearance information plays an essential role in the detection-to-track association, which lies at the core of the tracking-by-detection paradigm. While most existing works
consider the appearance distances between the detections and the tracks, they ignore the statistical information implied by the historical appearance distance records in the tracks, which can be particularly useful when a detection has similar distances with two or more tracks. In this work, we propose a hybrid track association (HTA) algorithm that models the historical appearance distances of a track with an incremental Gaussian mixture model (IGMM) and incorporates the derived statistical information into the calculation of the detection-to-track association cost. Experimental results on three MOT benchmarks confirm that HTA effectively improves the target identification performance with a small compromise to the tracking speed. Additionally, compared to many state-of-the-art trackers, the DeepSORT tracker equipped with HTA achieves better or comparable performance in terms of the balance of tracking quality and speed.
| Item Type: | Journal Article | |||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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| Divisions: | Faculty of Science > Computer Science | |||||||||
| Library of Congress Subject Headings (LCSH): | Automatic tracking -- Research, Optical pattern recognition, Computer vision, Tracking radar, Signal processing -- Digital techniques, Video surveillance, Pattern recognition systems, Automated vehicles | |||||||||
| Journal or Publication Title: | Pattern Recognition Letters | |||||||||
| Publisher: | Elsevier BV | |||||||||
| ISSN: | 0167-8655 | |||||||||
| Official Date: | June 2021 | |||||||||
| Dates: |
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| Volume: | 146 | |||||||||
| Page Range: | pp. 200-207 | |||||||||
| DOI: | 10.1016/j.patrec.2021.03.022 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||
| RIOXX Funder/Project Grant: |
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