Abstract：A method for human body detection from single image is presented. A hidden Markov model (HMM) is used to represent the human body. Based on the given series of human body configuration, the best image segments are inferred. Thus, the problem of human body detection is transformed into a HMM decoding one. Firstly, the image is segmented using Mean-Shift based procedure and the torso regions are searched according to color information. Secondly, the low-level features of shading, color and contour are combined to estimate the probability of feature matching and find the limb candidates. Finally, the connection probabilities of candidates are computed and the best fit human body regions are inferred by HMM decoding algorithm. The experimental results indicate that the proposed detection method detects reasonable human body well even from images with complex background and various pose. Compared with other detection methods, the proposed method approximates the body parts by rectangles and gets the integrally segmented human region. Moreover, it adapts to the low resolution images or images with people who are small or suffer from motion blur.
徐翠，郑颖，汪增福. 一种基于图像底层特征的隐马尔可夫人体检测方法*[J]. 模式识别与人工智能, 2009, 22(5): 743-749.
XU Cui, ZHENG Ying, WANG Zeng-Fu. Low-Level Image Features Based Human Body Detection Using Hidden Markov Model. , 2009, 22(5): 743-749.
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2009, Vol. 22 
