Multi-gait recognition using hypergraph partition

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• 作者：Xin Chen ; Jiaming Xu ; Jian Weng
• 关键词：Multi ; gait recognition ; Hypergraph partition ; Multi ; linear canonical correlation analysis ; Histogram of oriented gradients ; Edgelet
• 刊名：Machine Vision and Applications
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：28
• 期：1-2
• 页码：117-127
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Pattern Recognition; Image Processing and Computer Vision; Communications Engineering, Networks;
• 出版者：Springer Berlin Heidelberg
• ISSN：1432-1769
• 卷排序：28

文摘

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.