A METHOD FOR FOOTBALL SHOT MOTION RECOGNITION BASED ON STEREO VISION

Abstract

The core aim of motion-recognition technology is to detect moving targets and extract the target’s movement characteristics, classifying these so as to judge the corresponding action category. Moving target detection and tracking form the core of computer vision research topics in military visual guidance, robot visual navigation, industrial product detection, medical diagnosis, traffic monitoring, and virtual reality, and is also very important in terms of practical value and the broad prospects for development in other fields. However, in human behavior recognition algorithms, the background, light, environment, and other factors make identification accuracy difficult to obtain and unstable, with limited application scenarios. Because traditional target detection and tracking is based on monocular vision algorithm robustness, which is poor, this makes it difficult to detect and track moving targets in complex environments. In recent years, with the maturity of target detection and visual range-finding technology, pure static target visual barium distance or the depth of target detection cannot meet the demand of the market. Therefore, we need the efficient collection of deep information in moving target detection systems. Binocular stereo vision technology, as a hot research field in computer vision, can be widely used in the field of sports to achieve target detection and ranging functions. In view of this, this paper adopted a stereoscopic vision for use in target detection and tracking methods. This paper proposes a stereo-matching method based on hierarchical network minimum segmentation, where the method uses three-dimensional images from two layers of the pyramid decomposition by splitting the global optimal search strategy into a minimum at the top of the image to match with the initial parallax at the bottom of the image through regional gray correlation matching. Both reduce the search space of the stereo matching and improve matching accuracy. At the same time, this paper uses the motion of a football shot to prospect this recognition method, which can identify the target area. The Snake contour model method was also utilized to extract the target contour. This method has the following three characteristics: when ambient light changes slowly and suddenly, it can correctly detect moving targets; when the object is lit with cast shadows, the moving target can be detected correctly and is not affected by the shadows. When rigid target motion and deformation occur, it can correctly extract the target contour.