Exploiting Sub-region Deep Features for Specific Action Recognition in Combat Sports Video

Current research works for human action recognition in videos mainly focused on the case in different types of videos, that is coarse recognition. However, for recognizing specific actions of one object of interest, these methods may fail to recognize, especially if the video contains multiple moving objects with different actions. In this paper, we proposed a novel method for specific player action recognition in combat sports video. Object tracking with body segmentation are used to generate sub-frame sequences. Action recognition is achieved by training a new three-stream Convolutional Neural Networks (CNNs) model, where the network inputs are horizontal components of optical flow, single sub-frame and vertical components of optical flow, respectively. And the network fusion is applied at both convolutional and softmax layers. Extensive experiments on real broadcast combat sports videos are provided to show the advantages and effectiveness of the proposed method.