Adaptive Human Body Topology Guidance for Gait Recognition
Unlike appearance-based methods whose input may bring in some background noises,skeleton-based gait representation methods take key joints as input,which can neglect the noise interference.Meanwhile,most of the skeleton-based representation methods ignore the significance of the prior knowledge of human body structure or tend to focus on the local features.This study proposes a skeleton-based gait recognition framework,GaitBody,to capture more distinctive features from the gait sequences.Firstly,the study leverages a temporal multi-scale convolution module with a large kernel size to learn the multi-granularity temporal information.Secondly,it introduces topology information of the human body into a self-attention mechanism to exploit the spatial representations.Moreover,to make full use of temporal information,the most salient temporal information is generated and introduced into the self-attention mechanism.Experiments on the CASIA-B and OUMVLP-Pose datasets show that the method achieves state-of-the-art performance in skeleton-based gait recognition,and ablation studies show the effectiveness of the proposed modules.
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