Sparse Granger causality graphs for human action classification

Saehoon Yi, Vladimir Pavlovic

Abstract: Basic understanding and recognition of human actions can be accomplished by modeling the spatiotemporal relationship among major skeletal joints. In this work we present an approach that models human actions using temporal causal relations of joint movements. The relations form a graph with joints as nodes and edges induced by the Granger causality measure between pairs of joint point processes. Each human action is then represented by a distinct sparse causality graph. Experiments on motion capture data illustrate the robustness of this approach and its advantages over state-of-the-art methods.