Recognition method of dance rotation based on multi-feature fusion Online publication date: Wed, 26-Jan-2022
by Yang Liu; Meiyan Fan; Wenfeng Xu
International Journal of Arts and Technology (IJART), Vol. 13, No. 2, 2021
Abstract: There are some problems in traditional dance rotation recognition methods, such as low accuracy of contour superposition and low recognition rate. A dance rotation recognition method based on multi-feature fusion is proposed. The background noise subtraction method is used to separate the human motion regions in the foreground of the video data, and the contour features of each frame image of the preprocessed dance video are superimposed to obtain the direction gradient histogram features of the dance action information. According to the law of optical flow, the feature vectors of the histogram of optical flow direction are normalised. According to the shape and motion characteristics of human dance in dance video, the dance rotation recognition classifier is constructed to complete the dance rotation recognition based on multi-feature fusion. The experimental results show that the proposed method has higher accuracy of 97% and lower error rate of 0.7%.
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