Abstract
Aiming at the detection of athletes in sports videos, an automatic detection method based on AMNN is proposed. The background image from the image sequence is obtained, the moving area is extracted, and the color information of pixels to extract the green stadium from the background image is used. In order to improve the accuracy of athletes' detection, the texture similarity measurement method is used to eliminate the shadow in the movement area, the morphological method is used to eliminate the cracks in the area, and the noise outside the stadium is removed according to the stadium information. Combined with the images of nonathletes, a training set is constructed to train the NN classifier. For the input image frames, image pyramids of different scales are constructed by subsampling and the positions of several candidate athletes are detected by NN. The center of gravity of candidate athletes is calculated, a representative candidate athlete is obtained, and then, the final athlete position through a local search process is determined. Experiments show that the system can accurately detect the motion shape of moving targets, can process images in real time, and has good real-time performance.
Highlights
Detecting and tracking athletes in sports videos can provide important information for high-level sports video processing, such as motion analysis, event detection, and 3D reconstruction [1, 2]
A sports video athlete detection model has been put forward based on associative memory neural network (AMNN)
Based on the analysis of the current main moving target detection and tracking algorithms, this paper focuses on the methods of detecting and tracking athletes in sports videos and improves the traditional moving target detection and tracking algorithms
Summary
Detecting and tracking athletes in sports videos can provide important information for high-level sports video processing, such as motion analysis, event detection, and 3D reconstruction [1, 2]. It is of great significance to detect and track athletes in sports videos, which is conducive to various higher-level processing of videos, such as adding automatic explanation function to videos, quick retrieval of important events in videos, and tactical analysis of videos [5, 6]. Erefore, researchers have been inspired to do a lot of research on it, and many different methods have been put forward Another important direction in sports video analysis is the detection of exciting events. A sports video athlete detection model has been put forward based on associative memory neural network (AMNN). Suppose a group composed of M particles flies at a certain speed in the n-dimensional search space, and the state attributes of particle i in the t-th iteration are set as follows: Location: Xti xti, xti2, . . . , xtin,
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