Affiliations: School of Information Science, Japan Advanced Institute of Science and Technology, Japan. {s1020210, chen-fan, ikko}@jaist.ac.jp | Faculty of Information Technology, University of Science, VNU - Ho Chi Minh City, Vietnam. lhbac@fit.hcmus.edu.vn
Note: [] Address for correspondence: School of Information Science, Japan Advanced Institute of Science and Technology, Japan
Abstract: Emergence of novel techniques devices e.g., MS Kinect, enables reliable extraction of human skeletons from action videos. Taking skeleton data as inputs, we propose an approach to extract the discriminative patterns for efficient human action recognition. Each action is considered to consist of a sequence of unit actions, each of which is represented by a pattern. Given a skeleton sequence, we first automatically extract the key-frames, and then categorize them into different patterns. We further use a statistical metric to evaluate the discriminative capability of patterns, and define them as local features for action recognition. Experimental results show that the extracted local descriptors could provide very high accuracy in the action recognition, which demonstrate the efficiency of our method in extracting discriminative unit actions.
Keywords: Discriminative Patterns, 3D Skeletons, Local Descriptor, Improved TF-IDF, Action Recognition
