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Multi-gait recognition using hypergraph partition

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Abstract

Gait recognition is a challenging problem in computer vision, especially when multi-persons walk together, called as multi-gait recognition. Multi-gait recognition includes two aspects: participant segmentation and participant recognition. In this paper, we propose to segment each participant by hypergraph partition and recognize each participant by multi-linear canonical correlation analysis algorithm (UMCCA). Firstly, raw pixel areas are obtained by grid, and each pixel area is taken as a hypergraph vertex. Then HOG-based detection and tracking technology is used to calculate the weight of each hyperedge. After segmentation, UMCCA is used to extract gait features. Finally, identity of multi-gait is recognized. The experimental results demonstrate that our proposed method achieves good performance on multi-gait dataset.

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Authors and Affiliations

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

    Xin Chen & Jian Weng

  2. Baiyun District Bureau of Justice, Guangzhou, China

    Jiaming Xu

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  1. Xin Chen
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  2. Jiaming Xu
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  3. Jian Weng
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Correspondence to Xin Chen.

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Chen, X., Xu, J. & Weng, J. Multi-gait recognition using hypergraph partition. Machine Vision and Applications 28, 117–127 (2017). https://doi.org/10.1007/s00138-016-0810-6

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