Real-time human segmentation from RGB-D video sequence based on adaptive geodesic distance computation

In this paper, we propose a method for extracting humans in the foreground of video frames using color and depth information. To ensure real-time performance and to increase accuracy, we classify a video frame into two parts by degree of noise: head region with high noise level, and non-head region with low noise level. Then, we apply a high-computational geodesic matting algorithm to the noisy head region that includes hair, and a low-computational hole filling with smoothing method to other regions. Additionally, we modify the traditional color-based geodesic segmentation algorithm to consider additional depth information. Then, we apply temporal/spatial smoothing to the blended foreground mask in order to enhance the coherence between video frames. Experimental results show that the proposed method outperforms a previous approach by accuracy and performance.