GaitTAKE: Gait Recognition by Temporal Attention \\and Keypoint-guided Embedding

• URL: http://arxiv.org/abs/2207.03608v1
• Date: Thu, 7 Jul 2022 22:38:54 GMT
• Title: GaitTAKE: Gait Recognition by Temporal Attention \\and Keypoint-guided Embedding
• Authors: Hung-Min Hsu, Yizhou Wang, Cheng-Yen Yang, Jenq-Neng Hwang, Hoang Le Uyen Thuc, Kwang-Ju Kim
• Abstract summary: We propose a novel gait recognition framework, called Temporal Attention and Keypoint-guided Embedding (GaitTAKE) Experimental results show that our proposed method achieves a new SOTA in gait recognition with rank-1 accuracy of 98.0% (normal), 97.5% (bag) and 92.2% (coat)
• Score: 35.91172547755888
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gait recognition, which refers to the recognition or identification of a person based on their body shape and walking styles, derived from video data captured from a distance, is widely used in crime prevention, forensic identification, and social security. However, to the best of our knowledge, most of the existing methods use appearance, posture and temporal feautures without considering a learned temporal attention mechanism for global and local information fusion. In this paper, we propose a novel gait recognition framework, called Temporal Attention and Keypoint-guided Embedding (GaitTAKE), which effectively fuses temporal-attention-based global and local appearance feature and temporal aggregated human pose feature. Experimental results show that our proposed method achieves a new SOTA in gait recognition with rank-1 accuracy of 98.0% (normal), 97.5% (bag) and 92.2% (coat) on the CASIA-B gait dataset; 90.4% accuracy on the OU-MVLP gait dataset.

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