Abstract
Multi-object tracking (MOT) becomes a challenging task as non-linear motion and occlusion cause problems such as contaminated appearance, inaccurate positions and disturbed tracks. Despite great progress made by current trackers, their performance still needs improvement due to their inability to adapt their components to these challenges. In this work, we propose a new method, StrongOC-SORT, which exploits the observation-centric nature and four new modules to tackle these challenges more effectively. Specifically, we design an IoU-ReID Fusion module to minimize disruptions from rapid changes in direction. Moreover, we develop Dynamic Embedding and Observation Expansion modules that correspond to prevent track embedding from being contaminated by detection noise and to address the issue of slight overlap between observations under long-term lack of observations. Lastly, we propose an Active State module to provide discriminative tracks for association in DanceTrack. Our proposed method achieves state-of-the-art performance on DanceTrack and MOT20 with 63.4 HOTA and 64.1 HOTA, while providing competitive performance on MOT17 with the best IDF1 and AssA. The experimental results demonstrate the robustness and effectiveness of StrongOC-SORT under occlusion and non-linear motion.
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Acknowledgments
This work was supported in part by the Anhui Provincial Major Science and Technology Project (No. 202203a05020016), the National Key R &D Program of China (Nos. 2022YFB3303400 and 2021YFF0500900), and the National Natural Science Foundation of China (Nos. 71991464 and 61877056).
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Sun, Y., Huang, Z. (2024). StrongOC-SORT: Make Observation-Centric SORT More Robust. In: Hu, SM., Cai, Y., Rosin, P. (eds) Computer-Aided Design and Computer Graphics. CADGraphics 2023. Lecture Notes in Computer Science, vol 14250. Springer, Singapore. https://doi.org/10.1007/978-981-99-9666-7_8
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DOI: https://doi.org/10.1007/978-981-99-9666-7_8
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