DA-CCD: A novel action representation by Deep Architecture of local depth feature

With the widespread use of depth sensors, it is crucial to provide an effective and efficient solution for human action analysis applications upon the informative depth data. In this paper, we present a generic framework of modeling the human action by deep architecture enhanced local features with depth data. To introduce robust higher-level representations, we augment the adaptive and scalable local depth features in a deep feature learning manner. Specifically, a Deep Architecture of Comparative Coding Descriptor (DA-CCD) is proposed to represent the depth action data. Our approach obtains consistently superior recognition precisions on view specific/non-specific scenarios compared with other leading action representations of depth data on the Huawei/3DLife Dataset.