A survey on human action recognition using depth sensors

The recent advent of depth sensors opens up new opportunities to advance human action recognition by providing depth information. Many different approaches have been proposed for human action recognition using depth sensors. The main purpose of this paper is to provide a comprehensive study and an updated review on human action recognition using depth sensors. We give an overview of recent works in this field from the viewpoints of data modalities, feature extraction and classification. In terms of data modalities from depth sensors, recent approaches can be roughly categorized into depth map based and skeleton based approaches. Since depth maps encode 3D shape and appearance information, approaches based on depth maps are suitable for short simple actions and can achieve high performance. In contrast, due to the discriminative power and more concise form of skeletal joints, skeleton based approaches can model more complex actions, even in real time. This paper further provides a summary of the results obtained in the last couple of years on the public datasets. Moreover, we discuss limitations of the state of the art and outline promising directions of research in this area. The review assists in guiding both researchers and practitioners in the selection and development of approaches for human action recognition using depth sensors.