Players Detection using U-Net based Fully Convolutional Network

People detection in image and video is challenging problem that has great importance in different applications such as surveillance systems, autonomous driving systems, sports video analysis etc. Player detection task, as a subproblem of people detection, is one of the fundamental steps in football video analysis. In this paper we propose a method for player detection based on a fully convolutional neural network. Novelty in our approach is usage of U-Net architecture for generation of player probability map. U-Net consists of contracting path that has typical convolutional neural network architecture and extracting path where upsampled feature maps are combined with features from contracting path to obtain more precise segmentation. Next step is thresholding of player probability map followed by connected component analysis that gives player bounding boxes. Experimental results show promising performance on football field images including distant views, motion blur, complex background etc.