Human segmentation algorithm for real-time video-call applications

Seon Heo, Hyung Il Koo, Hong Il Kim, Nam Ik Cho

Abstract: This paper presents a human region segmentation algorithm for real-time video-call applications. Unlike conventional methods, the segmentation process is automatically initialized and the motion of cameras is not restricted. To be precise, our method is initialized by face detection results and human/background regions are modeled with spatial color Gaussian mixture models (SCGMMs). Based on the SCGMMs, we build a cost function considering spatial and color distributions of pixels, region smoothness, and temporal coherence. Here, the temporal coherence term allows us to have stable segmentation results. The cost function is minimized by the well-known graphcut algorithm and we update our SCGMM models with the segmentation results. Experimental results have shown that our method yields stable segmentation results with a small amount of computation load.