Automated Dance Movement Generation and Optimization Using Image Processing and Machine Learning

With the rapid development of technology and artificial intelligence, with this momentum, the sports industry also presents the characteristics of information technology, dance video has greatly enriched people’s daily life, but at the same time, it also puts forward greater requirements for video image processing technology, dance efficient action image processing and feature analysis has become a research hotspot in recent years. Image processing uses the computer to simulate and analyze the collected graphics, so as to obtain the corresponding three-dimensional information. Based on the introduction of how to identify the characteristics of dance movements and image analysis, this paper constructs a complete set of dance movement generation models and optimization methods. The number of movements is one of the key factors affecting the automatic generation of dance movements. The number of actions can directly affect the processing and optimization process of AI equipment. Under the influence of blurring the background and removing the noise, the automatic generation method of efficient dance movements will be more accurate. Under the machine estimation algorithm model, there is no obvious difference in various test data. Compared with the machine estimation algorithm model, the experimental data of the global contour algorithm model are slightly more prominent. The learning efficiency of the mobile computing model is all above 0.42, which is far superior to the data of the first two groups.