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• W2891342681 abstract "Trajectories and spatiotemporal attention model have been successfully used in skeleton-based action recognition. Most existing methods focus more attention on temporal structure mining. However, only a few local joints and their position features (e.g., critical position changes of hand, head, leg etc.) are responsible for the action label. In this work, we introduce a novel action recognition framework using Key Joints Selection and Spatiotemporal Mining, which can identify both key joints and their position & velocity histogram as well as trajectory features for action classification. First, histogram of human joints position and velocity are developed to enhance the spatiotemporal structure representation of existing trajectory-based methods. Second, the key joints are selected according to their information gains, and then their position & velocity histograms are weighted and composed with trajectory features to form one richer representation for final action classification. Experiments on two widely-tested benchmark datasets show that by combining the strength of both richer features and key joints selecting, our method can achieve state-of-the-art or competitive performance compared with existing results using sophisticated models such as deep learning, with advantages regarding the recognition accuracy and robustness." @default.
• W2891342681 created "2018-09-27" @default.
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• W2891342681 date "2018-10-01" @default.
• W2891342681 modified "2023-09-23" @default.
• W2891342681 title "Key Joints Selection and Spatiotemporal Mining for Skeleton-Based Action Recognition" @default.
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• W2891342681 doi "https://doi.org/10.1109/icip.2018.8451483" @default.
• W2891342681 hasPublicationYear "2018" @default.
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