Athlete Detection and Shadow Removal Algorithm in Track and Field Competition Based on Intelligent Optimization Algorithm

With the rapid development of today's video technology, new video coding standards are constantly being developed and widely used. Motion estimation is an important part of the video coding system, which can effectively remove the time redundancy between adjacent images in video linkage, and significantly improve coding efficiency. A large number of motion estimation calculations have significantly increased the computational complexity of the video coding system, so finding simple and efficient motion estimation algorithms has always been a research topic in the field of video coding. The current research on athletes' sports evaluation algorithms aims to understand how to effectively link faster sports evaluation algorithms with these new technologies to improve coding. By consulting a large number of literature and questionnaire surveys, this paper gives a detailed overview of the algorithm for the detection and extraction of sports targets and the shadow detection of sports targets. It studies the statistics of athletes' detection accuracy in track and field competitions in a sports college and investigates the statistics on a fitness APP. Detected data on the frequency of male and female track athletes exercising several times a month, and a fitness center uses intelligent optimization algorithms to detect the exercise data of male and female athletes, and the satisfaction of male and female users with the detection experience. Experiments show that the intelligent optimization algorithm proposed in this paper can effectively combine the motion detection characteristics of passengers and athletes with the concrete algorithm, select the appropriate particles, capture the appropriate termination strategy, and calculate the complexity, in order to improve the accuracy of the investigation and reduce the calculation Complexity will develop the same control points and appropriate design constraints.