DEPTH DATA ASSISTED HUMAN ACTION RECOGNITION: A FINE GRAINED SURVEY

Abstract

One of the most active research fields of computer vision [1] is video-assisted human action recognition. Since the depth data [2] obtained by Kinect cameras has more benefits than traditional RGB data, research on human action detection has recently increased because of the Kinect camera. We conducted a systematic study of strategies for recognizing human activity based on deep data in this article. All of the strategies are grouped into two categories: deep map tactics and skeleton tactics. A comparison of some of the more traditional strategies is also covered. Following that, we examined the specifics of different depth behavior databases and provided a straightforward distinction between them. We address the advantages and disadvantages of both depth and skeleton-based techniques in this discussion.