A Deep Vision Learning-Based Intelligent Recognition Method for Dynamic Sports Gestures

Nowadays, artificial intelligence-assisted automatic technological analyses of sports matches have received a more general demand. Among them, the effective recognition of sports actions from videos or images acts as the most fundamental issue to be solved. As sports gestures are characterized by remarkable specialty and instantaneity, it is required to develop specific effective recognition algorithms for this purpose. As a result, this work takes the case of Taekwondo sports as an example, and introduces deep vision learning to develop a specific intelligent recognition method for dynamic sports gestures. After sampling the key frames from dynamic videos, vital joint points of limbs are first obtained as the basic bottom features. Then, gesture expertise of this domain is formed to construct a multiview feature extraction work flow to better analyze complex characteristics of each gesture. Finally, the extracted feature representation is input into a recurrent neural network structure to output the discriminative results. At last, experiments are conducted for assessment, whose results show that the proposal can reach an accuracy of 95.6% and can be well suitable for the investigated problem scenario.