How good is kernel descriptor on depth motion map for action recognition

Abstract

This paper presents a new method for action recognition using depth data. Each depth sequence is represented by depth motion maps from three projection views (front, side and top) to exploit different aspects of the motion. However, different from state of the art works extracting local binary pattern or histogram of oriented gradients, we describe an action based on gradient kernel descriptor. The proposed method is evaluated on two benchmark datasets (MSRAction3D and MSRGestures3D) and obtains very competitive performances with the best state of the arts methods. Our best recognition rate is 91. 57% on MSRAction3D and 100% on MSRGestures3D dataset whereas [1] achieved 93. 77% and 94. 60% respectively.