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Multiple person tracking based on slow feature analysis

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Abstract

Object tracking is one of the most important components in numerous applications of computer vision. However, it still has many challenges to be solved, such as occlusion, matching, data association, etc. In this paper, we proposed to utilize slow feature analysis (SFA) method to handle the multiple person tracking problem. First, the part-based model is utilized to detect pedestrian in each frame. Then, a set of reliable tracklets is generated by utilizing spatial-temporal information of detection results. Third, SFA method is leveraged to extract slow-feature for these reliable tracklets. Finally, the traditional graph matching method is utilized to handle data association problem and consequently generate the final trajectory for individual tracking object. Some popular datasets are used in this study. The extensive comparison experiments demonstrate the superiority of the proposed method.

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Acknowledgment

This work was supported by the National High-Tech Research and Development Program of China (program 863, 2012AA10A401), the Grants of the Major State Basic Research Development Program of China (program 973, 2012CB114405), the National Key Technology R&D Program (2011BAD13B07 and 2011BAD13B04), the National Natural Science Foundation of China (31770904, 21106095), the Natural Science Foundation of Tianjin (15JCYBJC30700), the project of introducing one thousand high level talents in three years (5KQM110003), the Foundation for Introducing Talents to Tianjin Normal University (5RL123), the Academic Innovation Promotion Project of Tianjin Normal University for young teachers (52XC1403), the 131 Innovative Talents Cultivation of Tianjin (ZX110170) and Tianjin Normal University Application and Development Program (52XK1502).

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Authors and Affiliations

  1. Tianjin Key Laboratory of Animal and Plant Resistance/College of Life Sciences, Tianjin Normal University, Tianjin, 300387, China

    Tong Hao, Qian Wang, Dan Wu & Jin-Sheng Sun

  2. Tianjin Bohai Fisheries Research Institute, Tianjin, 300221, China

    Jin-Sheng Sun

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  1. Tong Hao
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  2. Qian Wang
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Correspondence to Tong Hao.

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Hao, T., Wang, Q., Wu, D. et al. Multiple person tracking based on slow feature analysis. Multimed Tools Appl 77, 3623–3637 (2018). https://doi.org/10.1007/s11042-017-5218-4

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  • DOI: https://doi.org/10.1007/s11042-017-5218-4

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