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- W2944571487 abstract "The study of human motion recognition has attracted many attentions in recent years. In this paper, a simple but effective feature combination is proposed by us for human action recognition based on skeletal points. The two features that make up this combination are the preprocessed 3D joint positions and the velocities of these 3D joints respectively. Then we use a combination of DTW [1], Fourier transform and SVM to model these actions and classify them. Because in the process of DTW, we use these two features to cooperate with each other to warp action samples, we call it the cooperative warp of two discriminative features (CWTDF). We also considered another scenario that we use these two features to warp their action samples respectively and then combine them before the classification process. We call it the separate warp of two discriminative features (SWTDF). But in this case, the classification performance is not as good as the cooperative warp situation. Although our feature representation is relatively simple, it is so sufficiently discriminative that our classification performance outperforms many state-of-the-art strategies based on skeletal points." @default.
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- W2944571487 date "2019-04-01" @default.
- W2944571487 modified "2023-09-25" @default.
- W2944571487 title "Cooperative Warp of Two Discriminative Features for Skeleton Based Action Recognition" @default.
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- W2944571487 doi "https://doi.org/10.1088/1742-6596/1187/4/042027" @default.
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