Dance Training Movement Depth Information Recognition Based on Artificial Intelligence

The motion capture system under artificial intelligence can avoid the deviation caused by artificial feature extraction. Human motion is composed of complex motion sequences, and capturing the time series is essential for motion recognition. The purpose of this paper is to provide a more perfect design for artificial intelligence movement recognition through the research on the recognition of movement depth information in dance training based on depth information. This paper firstly proposes a classification framework based on characteristics and data types, and comprehensively reviews the methods proposed in recent years. Subsequently, the performance of the algorithms described in the literature was compared and analyzed. Through the experiment of dance movement recognition, the quoted public test data set was summarized, and the accurate positioning rate was above 95%. Finally, the author discusses the future research direction and gives some Suggestions.