To address the issue of high recognition error in conventional action error detection methods, this article proposes a game of tennis serve error action detection algorithm based on feature point trajectory. To begin, a feature detection model for tennis serve images is established, followed by segmentation of the tennis serve images' multiscale features. Second, the path of the tennis serving image is effectively corrected, thereby raising the bar for tennis training and competition. Additionally, a visual feature acquisition system for tennis serving action is being developed using remote video monitoring in order to correct the path of the serving image during play. The corner mark of the serving action error point is determined using this algorithm, and the optimal modeling of the tennis serving image's path correction is realized using the developed edge segmentation algorithm. The results of simulations demonstrate that the aforementioned algorithm improves real-time performance and accuracy, and that it can accurately track players' visual edge information feature points while they are serving, conduct real-time evaluation and guidance via an expert system, effectively correct the tennis serving image path, and enhance your capacity for service.
