Abstract
Sports video analysis has produced many valuable applications driven by different needs, and in these applications, moving target detection technology plays an indispensable role. However, the uniqueness of sports videos brings a big challenge to target detection and tracking technology. Therefore, the purpose of this article is to propose an efficient multi-target detection algorithm to quickly and effectively detect all target objects in the video. We propose a multi-target detection and tracking framework based on a deep conditional random field network, adding a conditional random field layer to the output of the target detection network to model the mutual relationships and contextual information between targets. In addition, we also introduce local adaptive filters and spatial-temporal attention mechanisms into this framework to further improve target detection performance, especially when dealing with complex scenes and target interactions. Experimental results show that the proposed method is superior to the state-of-the-art methods in terms of accuracy and efficiency.