Deep OC-Sort: Multi-Pedestrian Tracking by Adaptive Re-Identification

Motion-based association for Multi-Object Tracking (MOT) has recently re-achieved prominence with the rise of powerful object detectors. Despite this, little work has been done to incorporate appearance cues beyond simple heuristic models that lack robustness to feature degradation. In this paper, w...

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Bibliographic Details
Published in2023 IEEE International Conference on Image Processing (ICIP) pp. 3025 - 3029
Main Authors Maggiolino, Gerard, Ahmad, Adnan, Cao, Jinkun, Kitani, Kris
Format Conference Proceeding
LanguageEnglish
Published IEEE 08.10.2023
Subjects
Adaptation models
Degradation
Detectors
Image matching
Image processing
Kalman filter
multi-object tracking
Tracking
Visualization
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Summary:Motion-based association for Multi-Object Tracking (MOT) has recently re-achieved prominence with the rise of powerful object detectors. Despite this, little work has been done to incorporate appearance cues beyond simple heuristic models that lack robustness to feature degradation. In this paper, we propose a novel way to leverage objects' appearances to adaptively integrate appearance matching into existing high-performance motion-based methods. Building upon the pure motion-based method OC-SORT, we achieve 1st place on MOT20 and 2nd place on MOT17 with 63.9 and 64.9 HOTA, respectively. We also achieve 61.3 HOTA on the challenging DanceTrack benchmark as a new state-of-the-art even compared to more heavily-designed methods. The code and models are available at https://github.com/GerardMaggiolino/Deep-OC-SORT.
DOI:10.1109/ICIP49359.2023.10222576