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• W4383500660 abstract "RGB-D based human action recognition has been a hot research topic with the release of RGB-D devices. Many attempts have been done to achieve robust and effective action recognition. This chapter reviews human action recognition techniques, including handcrafted feature representations extracted from different data modality and various deep neural network architectures. Moreover, commonly used action datasets, performance comparison, and promising future directions are presented." @default.
• W4383500660 created "2023-07-08" @default.
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• W4383500660 date "2023-07-07" @default.
• W4383500660 modified "2023-10-18" @default.
• W4383500660 title "RGB‐D Based Human Action Recognition: From Handcrafted to Deep Learning" @default.
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• W4383500660 doi "https://doi.org/10.1002/9781119863663.ch26" @default.
• W4383500660 hasPublicationYear "2023" @default.
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