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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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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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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DOI: https://doi.org/10.1007/s11042-023-15231-1