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Citation:
Yeong-Seok Kim, Jong-Chul Yoon, and In-Kwon Lee, "Real-time Human Segmentation from RGB-D Video Sequence based on Adaptive Geodesic Distance Computation", Multimedia Tools and Applications (SCIE). doi:10.1007/s11042-017-5375-5, November 2017
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Abstract:
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.
