Target Tracking Algorithm of Basketball Video Based on Improved Grey Neural Network

Considering the limitation of tracking range on sports video target tracking in basketball games, there are some problems, such as poor tracking effect, low accuracy, low anti-interference ability, and being time-consuming. Therefore, this study proposes a target tracking algorithm of basketball video based on improved grey neural network. According to the pixel grey difference of the target image in basketball video, this study applies the adaptive threshold algorithm in order to segment the target image of basketball video and obtain the target area of basketball video. This algorithm can normalize the grey level of the target area, build the generating sequence of the target area, and collect the target data of the basketball video. It obtains the feature output matrix of basketball video target based on the geometric dispersion of the target image and extracts the key feature points of basketball video target by single frame visual difference analysis. In addition, it makes use of the improved grey neural network to track and locate the feature points of basketball video target and reconstruct the basketball video target image with superresolution to realize basketball video target tracking. The experimental results show that the proposed algorithm has good target tracking effect of basketball video, can effectively improve the target tracking accuracy and anti-interference ability, and can shorten the target tracking time.