3D flow estimation for human action recognition from colored point clouds

Motion perception and classification are key elements exploited by humans for recognizing actions. The same principles can serve as a basis for building cognitive architectures which can recognize human actions, thus enhancing challenging applications such as human robot interaction, visual surveillance, content-based video analysis and motion capture. In this paper, we propose an autonomous system for real-time human action recognition based on 3D motion flow estimation. We exploit colored point cloud data acquired with a Microsoft Kinect and we summarize the motion information by means of a 3D grid-based descriptor. Finally, temporal sequences of descriptors are classified with the Nearest Neighbor technique. We also present a newly created public dataset for RGB-D human action recognition which contains 15 actions performed by 12 different people. Our overall system is tested on this dataset and on the dataset used in Ballin, Munaro, and Menegatti (2012), showing the effectiveness of the proposed approach in recognizing about 90% of the actions.