Abstract
The article proposes a new multi-person pose tracking-by-detection method capable of very fast execution without use of GPU acceleration. Starting from a common online approach to object tracking, based on graph bipartite matching, we further improve the method with several novel additions. First, linear regression is used to estimate the movement of a pose root point, allowing the tracker to better preserve identity recognition despite missing detections. It is combined with a new cost function term based on per-joint distance to improve accuracy in scenes with high densities of individuals. Additionally, an alternative for visual feature matching without the use of neural networks is provided. Experiments on PoseTrack dataset have shown that proposed method outperforms other current tracking-by-detection methods based on graph bipartite matching and achieves tracking quality close to the state-of-the-art in general.
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The reported study was funded by Russian Foundation of Basic Researches, project number 19-29-09090.
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Gilya-Zetinov, A., Bugaev, A., Khelvas, A., Konyagin, E., Segre, J. (2022). High-Speed Multi-person Tracking Method Using Bipartite Matching. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 283. Springer, Cham. https://doi.org/10.1007/978-3-030-80119-9_51
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