Video-Based Human-Posture Monitoring From RGB-D Cameras

Correct pose/posture is crucial in most human activities, and increasingly in using computer screens of many form factors. In this paper, we build a spatiotemporal reasoning infrastructure on top of standard Computer Vision (CV) algorithms to provide an alternate, much more accurate, faster method for tracking correct posture than pure deep learning (DL) methods. We use CV to determine poses of the 2D human stick models from RGB images, which are further enhanced using depth information (from RGB-D camera) to determine relevant angles and compare them against the standards. By applying our method to two very different posture applications (knowledge worker and taekwondo), we show that it outperforms all others, including machine learning, deep learning, and time series-based prediction. Furthermore, superior performance is seen not only in the estimation accuracy but also in the estimation speed.