Player target tracking and detection in football game video using edge computing and deep learning

The purpose is to explore the player detection and motion tracking in football game video based on edge computing and deep learning (DL), thus improving the detection effect of player trajectory in different scenes. First, the basic technology of player target tracking and detection task is analyzed based on the Histograms of Oriented Gradients feature. Then, the neural network structure in DL is combined with the target tracking method to improve the miss detection problem of the Faster R-CNN (FRCN) algorithm in detecting small targets. Edge computing places massive computing nodes close to the terminal devices to meet the high computing and low latency requirements of DL on edge devices. After the occlusion problem in the football game is analyzed, the optimized algorithm is applied to the public dataset OTB2013 and the football game dataset containing 80 motion trajectories. After testing, the target tracking accuracy of the improved FRCN is 89.1%, the target tracking success rate is 64.5%, and the running frame rate is still about 25 fps. The high confidence of FRCN algorithm also avoids template pollution. In the ordinary scene, the FRCN algorithm basically does not lose the target. The area under curve value of the proposed FRCN algorithm decreases slightly in the scene where the target is occluded. The FRCN algorithm based on DL technology can achieve the target tracking of players in football game video and has strong robustness to the situation of players occlusion. The designed target detection algorithm is applied to the football game video, which can better analyze the technical characteristics of players, promote the development of football technology, bring different viewing experiences to the audience, drive the development of economic products derived from football games, and promote the dissemination and promotion of football.