The representation and selection of action features directly affect the recognition effect of human action recognition methods.\nSingle feature is often affected by human appearance, environment, camera settings, and other factors. Aiming at the problem that\nthe existing multimodal feature fusion methods cannot effectively measure the contribution of different features, this paper\nproposed a human action recognition method based on RGB-D image features, which makes full use of the multimodal information\nprovided by RGB-D sensors to extract effective human action features. In this paper, three kinds of human action\nfeatures with different modal information are proposed: RGB-HOG feature based on RGB image information, which has good\ngeometric scale invariance; D-STIP feature based on depth image, which maintains the dynamic characteristics of human motion\nand has local invariance; and S-JRPF feature-based skeleton information, which has good ability to describe motion space\nstructure. At the same time, multiple K-nearest neighbor classifiers with better generalization ability are used to integrate decisionmaking\nclassification. The experimental results show that the algorithm achieves ideal recognition results on the public G3D and\nCAD60 datasets.
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