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Multi-gait identification based on multilinear analysis and multi-target tracking

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Abstract

A new gait pattern is addressed and recognized in this paper. We use a multi-view part detector to detect the body parts of each participant. Multi-gait consisting of more than one participant is tracked using hierarchical association. We use a high-dimension exemplar-based method to realize gait image inpainting and use a tensor’s lowest rank to complete a two-value sequence completion. We use multiple linear tensors to describe multi-gait and realize recognition by a segmented accumulated energy map. The experimental results indicate that the methodology achieves high multi-gait recognition accuracy and has good robustness to dress, carried objects and view variance.

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Authors and Affiliations

  1. Department of Information Science and Technology, Jinan University, Guangzhou, 510000, China

    Xin Chen, Tianqi Yang & Jiaming Xu

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  1. Xin Chen
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  2. Tianqi Yang
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  3. Jiaming Xu
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Correspondence to Xin Chen.

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Chen, X., Yang, T. & Xu, J. Multi-gait identification based on multilinear analysis and multi-target tracking. Multimed Tools Appl 75, 6505–6532 (2016). https://doi.org/10.1007/s11042-015-2585-6

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  • DOI: https://doi.org/10.1007/s11042-015-2585-6

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