Computer vision is an interesting branch of artificial intelligence which is dedicated to how electronic devices can achieve the level of capabilities to perceive things just like ordinary human beings do. In order to solve the poor effect of video for the detection of target in football matches and the low accuracy of target tracking, this paper aims to make a deep exploration of the methods of video for the detection of target and tracking in football matches. The video moving for the detection of target method based on background model is used to extract the image in the background of the matching video which improves the light flow field. Secondly, the video differential image is acquired according to the difference of colors, the ghost target of the image in the video background model is scientifically determined, the ghost degree of the pixel points of the image is scientifically determined, and the flicker matrix of the target image is constructed. The number of pixels of the moving target is derived. A meanshift-based video target tracking algorithm is used in conjunction for the detection of target result to determine whether to track the target image until the overall video target tracking task is completed, move the central position of the target frame and background frame to the target position, select the best one to adapt to the target change, and determine whether to track the target image until the overall video target tracking task is completed. The simulation results suggest that the approach described in this study is capable of detecting and tracking moving objects, as well as improving target recognition and tracking accuracy.

This paper aims to determine relationships between 160 matches statistics and the match results in two match stages of 2020 CSL under the COVID-19 pandemic prevention and control. A team's winning probability was evaluated by a twostandard-deviation increase in the value of each variable. The smallest worthwhile change was used to evaluate nonclinical magnitude-based inferences. The results showed that for group round robin stage, nine match statistics had clearly positive effects on the probability of winning (Shot, Shot on Target, Shot from Set Piece, Cross Accuracy, Counterattack, Won Challenge, Tackle Gaining, HIR Distance in BP, Sprinting Distance in BP), two had obviously negative effects (Distance Covered in Penalty Area, Sprinting Distance Out of BP), other twenty-three statistics had either trivial or unclear effects. While for the knockout stage, the effects of nine match statistics (Pass Accuracy, Forward Pass Accuracy, Delivery into Attacking Third, Delivery into Penalty Area, Dribble into Attacking Third, Corner, Foul Committed, Yellow Card, Distance Covered in Attacking Third) turned to clearly positive, the effects of Won Challenge, Cross Accuracy turned to trivial and clearly negative, respectively. Coaches and players should take these different aspects into account when planning practices and competitions for their teams.