Football Player Posture Detection Method Combining Foreground Detection and Neural Networks

In recent years, with the rapid development of artificial intelligence, information technology, intelligent digital video surveillance systems, real-time sports competition playback, and other technologies have emerged one after another, making the advantages of deep learning-based football posture detection tasks become more obvious. Related models and methods have been applied to the research field of sports posture estimation and have achieved great improvement, surpassing the traditional football posture estimation method based on manual design features in one fell swoop. In addition, the application of video foreground detection has developed rapidly and has great application value in sports analysis. Therefore, this paper proposes a novel football motion detection approach combining foreground detection and deep learning for real-time detection of football player posture. The main task of foreground target detection is to extract the interesting foreground target in the real monitoring scene and use it as the target of interest for subsequent analysis. Then, we propose a triple DetectNet detection framework based on deep learning technology, which can quickly and robustly realize the three-dimensional pose estimation of multiperson motion. For input, the triple DetectNet framework uses three neural networks and is executed in three stages; the first stage is to use the DetectNet (DN) network to detect the bounding box of each person separately, the second stage uses the 2DPoseNet (2DPN) network to estimate each of the corresponding two-dimensional poses of the individual, and the third stage uses the 3DPoseNet (3DPN) network to obtain the 3D pose of the person. This paper also conducted experiments on four datasets, and the results proved the superiority and success of this algorithm.