Action recognition using multi-directional projected depth motion maps

Abstract

Camera-based action recognition plays a key role in diverse computer vision applications such as human computer interaction. This paper proposes a new action recognition approach using multi-directional projected depth motion map based motion descriptors. First, for the input depth video sequence, all the depth frames in the video are projected onto multiple planes to form the projected images. The absolute difference between two consecutive projected images is accumulated through the entire depth video for establishing maps from multiple views. Then, the local motion consistency of the map is examined to form a histogram of local binary patterns, which are then concatenated and further incorporated into a kernel-based extreme learning machine for action recognition. In contrast to that only three directions are used to calculated the projected depth images for motion feature extraction in the conventional approaches, the proposed approach is able to provide an effective and flexible framework to examine the depth motion maps in multiple projected directions. The proposed approach is evaluated in the well-known MSRA action and gesture video benchmark datasets to demonstrate its superior performance.