A Hybrid Deep Learning-Based Intelligent System for Sports Action Recognition via Visual Knowledge Discovery

The intelligent recognition systems for sports actions have been a more general demand, so as to facilitate technical analysis of health management. This highly relies on deep analysis towards frame-level image data from the perspective of visual knowledge discovery. In recent years, the rapid development of deep learning technology has well boosted a number of technical breakthrough in computer vision. In this context, this work takes aerobics as the main object, and proposes a hybrid deep learning-based intelligent system for sports action recognition via visual knowledge discovery. Specifically, the human skeleton is represented as a graph based on the physical structure of the human body in this paper, and the selective hypergraph convolution network is selected to adaptively extract the multi-scale information in the skeleton. And the selective-frame temporal convolution is specially selected for the situation to construct recognition model. Upon the basis of proper feature extraction, a triple loss-based error measurement method is employed to construct objective function, and a recurrent neural network structure is further developed to model dynamic action sequence characteristics. The data source of this article is mainly the private data compiled by the research group. Finally, experiments are carried out on the CMU motion capture dataset, and the effectiveness of the proposed algorithm is verified by comparing the experimental results with those of the existing algorithms.