Football matches not only showcase athletes’ skills and spirit but also foster sports culture and social cohesion. This study designs an improved Faster Region-based Convolutional Neural Network-based target detection model to extract football players’ movement routes. The model enhances feature extraction using residual and feature pyramid networks and optimizes Anchor boxes with binary K-means clustering. A similarity matrix integrating motion and appearance features is then used for movement route extraction. The results show that the accuracy and recall of the detection model are excellent, and the intersection over union ratio and precision mean are also better than the comparison model. Meanwhile, in various scenarios, the accuracy of the mobile route extraction algorithm is at a high level, and the average frame rate performs well. The results demonstrate the effectiveness of the detection model and extraction algorithm in analyzing player movement, providing valuable insights for tactical analysis.