Inverse Dynamics for Action Recognition.

Pose-based approaches for human action recognition are attractive owing to their accurate use of human motion information. Traditionally, such approaches used kinematic features for classification. However, in addition to having high dimensions and a small interclass variation, kinematic features do not consider the interaction of the environment on human motion. In this paper, we propose a method for action recognition using dynamic features, derived by applying inverse dynamics to a physics-based representation of the human body. The physics-based model is articulated and actuated with muscles and consists of joints with variable stiffness. Dynamic features under consideration include the torques from the knee and hip joints of both legs and, implicitly, gravity, ground reaction forces, and the pose of the remaining body parts. These features are more discriminative than kinematic features, resulting in a low-dimensional representation for human actions, which preserves much of the information of the original high-dimensional pose. This low-dimensional feature achieves good classification performance even with a relatively small training data set in a simple classification framework such as a hidden Markov model. The effectiveness of the proposed method is demonstrated through experiments on the Carnegie Mellon University motion capture data set and Osaka University Kinect action data set with various actions.