The tracking technology of the 3D space position of the target is of great use to the analysis and understanding of professional volleyball match, but there are many factors that affect the volleyball tracking rate in the competition. In this paper, the image processing technology is combined with 3D spatial matching technology, and a sports volleyball tracking system framework is established with particle filter, which improves the accuracy of volleyball tracking more effectively. This article begins with a 3D tracking volleyball workflow diagram and selects using particle filters to improve tracking success rates. Secondly, the paper puts forward the similarity estimation method according to the characteristics of volleyball itself, mainly the similarity estimation of the base and HSV color space, to solve the problem of the supercomputing of real-time tracking. Finally, the results of the simulation experiment can be seen that the method can greatly improve the tracking success rate, high efficiency and high accuracy of the characteristics of the dynamic video sports volleyball tracking, has an unpredictable application development value.INDEX TERMS Image processing, 3D spatial matching, particle filters, tracking technology.

Physical data is an important aspect of urban data, which provides a guarantee for the healthy development of smart cities. Students’ physical health evaluation is an important part of school physical education, and postural recognition plays a significant role in physical sports. Traditional posture recognition methods are with low accuracy and high error rate due to the influence of environmental factors. Therefore, we propose a new Kinect-based posture recognition method in a physical sports training system based on urban data. First, Kinect is used to obtain the spatial coordinates of human body joints. Then, the angle is calculated by the two-point method and the body posture library is defined. Finally, angle matching with posture library is used to analyze posture recognition. We adopt this method to automatically test the effect of physical sports training, and it can be applied to the pull-up of students’ sports. The position of the crossbar is determined according to the depth sensor information, and the position of the mandible is determined by using bone tracking. The bending degree of the arm is determined through the three key joints of the arm. The distance from the jaw to the bar and the length of the arm are used to score and count the movements. Meanwhile, the user can adjust his position by playing back the action video and scoring, so as to achieve a better training effect.