结合局部和全局特征的人群密度估计算法  

Crowd density estimation algorithm combining local and global features

作　　者：李寅[1] 王贵锦[1] 林行刚[1] 

机构地区：[1]清华大学电子工程系,北京100084

出　　处：《清华大学学报（自然科学版）》2013年第4期542-545,549,共5页Journal of Tsinghua University(Science and Technology)

基　　金：国家自然科学基金重点项目(61271390;61132007)

摘　　要：传统的人群密度估计算法由于受到背景噪声及遮挡等因素的影响,不能同时对不同密度等级的人群做出准确估计。该文提出一种结合局部和全局特征的混合型人群密度估计算法。首先,对输入图像进行预处理以减少背景噪声干扰;其次,计算前景团块像素数占全幅图像的比例,并引入阈值分割机制;最后,对阈值上下的图像分别采用基于全局特征的分类算法和基于局部特征的回归算法来获取人群密度。此外,对基于全局特征的分类算法,提出结合小波变换和灰度共生矩阵的纹理特征描述子来提高分类准确率。实验结果表明该文算法对不同密度等级的人群均能做出准确估计且鲁棒性强。Clutter and occlusions hinder conventional crowd density estimation algorithms from yielding accurate results for different crowd density levels. A hybrid approach is presented here with combines local and global features. First, preprocessing of the input images is used to reduce the background noise. Then, the ratio of foreground blobs to the whole image is calculated with a mechanism for threshold segmentation. Finally, a regression algorithm based on the local features is used to analyze images below the threshold with a classification algorithm based on global features images used to analyze images above the threshold. A texture descriptor combining wavelet transforms and a gray level co occurrence matrix is then used to improve the classification accuracy for the classification algorithm based on the global features. Tests demonstrate that this method is both accurate and robust for different crowd density levels.

关 键 词：人群密度估计 阈值分割 线性回归 小波变换 灰度共生矩阵 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]