一种基于图像底层特征的隐马尔可夫人体检测方法

Low-Level Image Features Based Human Body Detection Using Hidden Markov Model

提出一种单幅图像中的人体检测方法.该方法用隐马尔可夫模型表示人体,根据给定的人体结构序列估计产生该序列的图像区域,从而将人体检测问题转化为隐马尔可夫解码问题求解.首先对图像进行Mean-Shift分割,并根据颜色信息搜索出属于躯干的区域,然后将明暗度、颜色及边缘3种底层特征相结合,估计特征匹配概率并由此获得四肢部分的候选区域.最后估计候选区域的连接概率并利用隐马尔可夫解码算法找出最优的人体配置区域.实验结果表明,该方法对于复杂背景中具有不同姿态的人体图像可得到较满意的检测结果.和其它检测方法相比,该方法并非单纯地给出矩形近似的人体各个部分,同时还获得较完整分割的人体图像.尤其对于图像分辨率较低、图像中的人体较小且存在运动模糊的情况,该方法能够获得较好的检测结果.

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.