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Modern science and technology is highly differentiated and concentrated, and has also entered a new stage of multidisciplinary comprehensive utilization. Especially, VR technology has played a great role in competitive sports training. Intelligent video analysis system is a key point of modern video development. With the development of computer technology and the gradual progress of video technology, intelligent video analysis system has broad development space. In this paper, a key algorithm SISA (Sports Image Segmentation Algorithm) is designed. The algorithm improves and optimizes the semantic segmentation algorithm by using hole convolution, pyramid pooling and trainable conditional random field structure, and uses key node detection algorithm to enhance the segmentation effect of small objects. The common methods to realize virtual human animation are manual marking, motion capture and automatic recognition. In order to reduce the workload of manual marking, avoid the high cost of motion capture and make up for the precision error of automatic recognition, it can be practically applied to the guidance of physical training.

Development significance and structure analysis of sports video image analysis system based on VR