Pose-Enhanced Relation Feature for Action Recognition in Still Images

Due to the lack of motion information, action recognition in still images is considered a challenging task. Previous works focused on contextual information in the image, including human pose, surrounding objects, etc. But they rarely consider the relation between the local pose and the entire human body, so that poses related to the action are not fully utilized. In this paper, we propose a solution for action recognition in still images, which makes complete and effective use of pose information. The multi-key points calculation method is carefully designed for generating pose regions that explicitly includes possible actions. The extensible Pose-Enhanced Relation Module extracts the implicit relation between pose and human body, and outputs the Pose-Enhanced Relation Feature which owns powerful representation capabilities. Surrounding objects information is also applied to strengthen the solution. Through experiments, it can be found that the proposed solution exceed the state-of-the-art performance on two commonly used datasets, PASCAL VOC 2012 Action and Stanford 40 Actions. Visualization shows that the proposed solution enables the network to pay more attention to the pose regions related to the action.