A model-based approach for human head-and-shoulder segmentation

Xiaowei Deng, Yuxiang Shen, Xiaolin Wu, Liang Zhao

Abstract: Object boundary extraction has long been a fundamental research topic, as well as an essential component in many visual computing and communication algorithms, such as computer vision, robotics, pattern recognition and video compression. Under this topic, human head-and-shoulder segmentation is of particular meaning, given the ubiquity of head-and-shoulder type of videos in social media, teleconferencing, and entertainment. Although human visual system can easily detect and recognize the head and upper body of a person, this seemingly simple task still poses a challenge to computers. In this paper, an effective and efficient segmentation method is proposed. This method consists of a novel human body descriptor in polar coordinates and a Markov chain based boundary model, which work together to generate precise boundary results. Moreover, dynamic programming is employed in this work, so as to accelerate the segmentation process. Comparisons with other algorithms are made in the experimental part, which clearly exhibits the advantage of our proposed method over some of its precedents.