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- W4383333708 abstract "To solve the problem of low accuracy of gait recognition in complex scenes, a novel skeleton-based gait recognition algorithm, GCGait, is proposed. Taking human posture as the input of gait feature, the interference caused by wearing changes and other factors is reduced. To extract sufficient input features, multi-branch input is used in the early stage of the model. By introducing the multi-attention mechanism, the network can learn the semantic information of the non-directly connected joints, excavate the most discriminative features from complex videos, and further improve the recognition performance. In order to reduce the influence of cross view, the fusion loss function is used in the experiment. Experimental results show that the average recognition rate of the proposed algorithm on the CASIA-B dataset is improved by 5.2%, and the average recognition accuracy on the OU-MVLP dataset is increased by 66.3%, which proves the effectiveness of the proposed method." @default.
- W4383333708 created "2023-07-07" @default.
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- W4383333708 date "2023-05-26" @default.
- W4383333708 modified "2023-10-14" @default.
- W4383333708 title "Gait Recognition Algorithm GCGait based on Skeleton" @default.
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- W4383333708 doi "https://doi.org/10.1109/iciba56860.2023.10165257" @default.
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