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.

To explore the intervention effect of two different physical teaching modes on college students' physical exercise attitude and provide new perspectives and approaches for the development of physical teaching in colleges and universities. Methods A total of 51 Grade 2020 students from a university in Guangzhou were selected as the experimental subjects and were enrolled in a single-blind experiment. They were divided into the traditional teaching class (31students) and the "online and offline" blended teaching class (30 students) for 18 weeks. Results SPSS 25.0 was used to perform an independent sample T-test and paired sample T-test for the data obtained from the questionnaire, the results show that: (1) Compared with that before the experiment, the physical attitude of students in the traditional teaching class was significantly improved after the experiment (p = 0.046 ⪅0.05); the physical attitude of students in the "online and offline" blended teaching class was considerably improved after the experiment (p = 0.007 ⪅0.01). After the experiment, the physical attitude of students in the "online and offline" blended teaching class was better than that of students in the traditional teaching class. (2) The physical behavioral intention of students in the traditional class changed significantly compared with that before the experiment, which was statistically significant (p ⪅0.05), as shown by a higher intention to participate in physical exercise. (3) The behavioral attitude, the sense of behavioral control, and subjective criteria of the students in the "online and offline" blended teaching class were statistically significant (p ⪅ 0.01 or p ⪅ 0.05), which was reflected in an increased tendency to participate in physical exercise, an increased ability to control physical exercise behavior and a more accurate subjective evaluation of participation in physical exercise. Both teaching modes can effectively correct students' attitudes towards participation in physical exercise; but the effect of "online and offline" blended teaching on improving students' physical exercise attitude was significantly better than that of traditional teaching.