Deep Clustering Efficient Learning Network for Motion Recognition Based on Self-Attention Mechanism

Abstract

Multi-person behavior event recognition has become an increasingly challenging research field in human–computer interaction. With the rapid development of deep learning and computer vision, it plays an important role in the inference and analysis of real sports events, that is, given the video frequency of sports events, when letting it analyze and judge the behavior trend of athletes, often faced with the limitations of large-scale data sets and hardware, it takes a lot of time, and the accuracy of the results is not high. Therefore, we propose a deep clustering learning network for motion recognition under the self-attention mechanism, which can efficiently solve the accuracy and efficiency problems of sports event analysis and judgment. This method can not only solve the problem of gradient disappearance and explosion in the recurrent neural network (RNN), but also capture the internal correlation between multiple people on the sports field for identification, etc., by using the long and short-term memory network (LSTM), and combine the motion coding information in the key frames with the deep embedded clustering (DEC) to better analyze and judge the complex behavior change types of athletes. In addition, by using the self-attention mechanism, we can not only analyze the whole process of the sports video macroscopically, but also focus on the specific attributes of the movement, extract the key posture features of the athletes, further enhance the features, effectively reduce the amount of parameters in the calculation process of self-attention, reduce the computational complexity, and maintain the ability to capture details. The accuracy and efficiency of reasoning and judgment are improved. Through verification on large video datasets of mainstream sports, we achieved high accuracy and improved the efficiency of inference and prediction. It is proved that the method is effective and feasible in the analysis and reasoning of sports videos.