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Visual detection and tracking algorithms for human motion

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Abstract

In dense scenes, a large number of individuals can introduce serious complications for motion detection, such as blurred vision, chaotic scenes, and complex behaviours. For low-density pedestrian detection and tracking algorithms, the accuracy is greatly reduced for both detection and tracking. High-density detection or tracking fails too when these problems are encountered in high-density scenes. In light of the above problems, a detection algorithm and a tracking algorithm based on the human head and shoulder model are proposed. A support vector machine is used to train the classifier by machine learning. The detection algorithm proposed in this paper achieves a detection accuracy of 94% by using the MIT and INRIA datasets. The average accuracy of pedestrian tracking in high-density scenes is approximately 95%.

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Data availability

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to their containing information that could compromise the privacy of research participants.

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Acknowledgements

This research was financially supported by the Major Scientific Research Project for Universities of Guangdong Province (2020ZDZX3058); Guangdong Provincial Special Funds Project for Discipline Construction (No. 2013 WYXM0122); Science and Technology Projects of Zhuhai in the Field of Social Development (2220004000066); Key Laboratory of Intelligent Multimedia Technology (201762005)

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  1. Key Laboratory of Intelligent Multimedia Technology, Research Center for Intelligent Engineering and Educational Application, Beijing Normal University, Zhuhai, 519087, China

    Ge Yang & Siping Chen

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  1. Ge Yang
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  2. Siping Chen
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Correspondence to Ge Yang.

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We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, and there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.

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Yang, G., Chen, S. Visual detection and tracking algorithms for human motion. Multimed Tools Appl 82, 47165–47188 (2023). https://doi.org/10.1007/s11042-023-15231-1

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