Dynamic Motion Representation for Human Action Recognition

Sadjad Asghari-Esfeden, Mario Sznaier, Octavia I. Camps

Abstract: Despite the advances in Human Activity Recognition, the ability to exploit the dynamics of human body motion in videos has yet to be achieved. In numerous recent works, researchers have used appearance and motion as independent inputs to infer the action that is taking place in a specific video. In this paper, we highlight that while using a novel representation of human body motion, we can benefit from appearance and motion simultaneously. As a result, better performance of action recognition can be achieved. We start with a pose estimator to extract the location and heat-map of body joints in each frame. We use a dynamic encoder to generate a fixed size representation from these body joint heat-maps. Our experimental results show that training a convolutional neural network with the dynamic motion representation outperforms state-of-the-art action recognition models. By modeling distinguishable activities as distinct dynamical systems and with the help of two stream networks, we obtain the best performance on HMDB, JHMDB, UCF-101, and AVA datasets.