Notice of Violation of IEEE Publication Principles: Study on Sports Volleyball Tracking Technology Based on Image Processing and 3D Space Matching

Abstract

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.

Analysis and System Construction of ALSTM-LSTM Model-based Sports Jumping Rope Movement

“…A similarity estimation method was proposed according to the characteristics of volleyball. The method has good tracking performance and its success rate exceeds 80% [22]. Jalal introduced a pseudo-2D model to the original human pose estimation method to achieve the extraction of contour features and pose point features He introduced a K-ary tree hashing algorithm to optimize the data set.…”

Vision-based movement recognition reveals badminton player footwork using deep learning and binocular positioning

“…Urtasun et al [21] used monocular cameras to track the golf swing mode used by participants. He et al [22] extracted the three-dimensional volleyball trajectory from a game video using color tracking and 3D space matching. Host et al [23] were able to distinguish multiple players by their performance in handball games by using a multi-target tracking algorithm.…”

“…He et al. [ 22 ] extracted the three-dimensional volleyball trajectory from a game video using color tracking and 3D space matching. Host et al.…”