Key Point Detection in Football Match Based on Convolutional Neural Network

抄録

Key point detection in football matches can provide effective support for understanding football videos and recognizing player movements. This study analyzes the position information of players in football matches using key point detection methods. A cascaded convolutional neural network for detection and regression is combined to design a key point detection model for the impact on football game commentary and player control rate. A first level key point detection network based on ResNet and a second level regression network based on heatmap are proposed by combining the fused stacked feature extraction and optimizing the original structure. The experiment confirmed that the model has an accuracy of 95.62% in detecting key points. At the same time, the coordinate error near the goal and other areas is relatively small. However, there is a significant coordinate error in the size exclusion zone. This research provides a certain reference for the analysis of future football matches.