作为计算机视觉以及智能车辆领域的一个重要研究方向,车载视觉系统中的行人检测技术近年来得到了业界广泛关注。本文对2005年以来该技术中最重要的两个环节——感兴趣区域分割以及目标识别的研究现状进行综述,首先将感兴趣区域分割的典型方法按照分割所用信息的不同进行分类并对比它们的优缺点,之后对行人目标识别的特征提取、分类器构造以及搜索框架等方面的进展进行总结,最后对未来发展作出展望。

As one of the most important areas of research in the domain of computer vision and intelligent vehicles, pedestrian detection of on-board vision systems has attracted extensive interest from the research community. In order to show the general idea of the latest development of this technology, we focus on the improvement of the two major modules since 2005: regions of interest (ROIs) segmentation and object recognition. First, we classify the typical methods in the field of ROIs segmentation according to the information used in segmentation and analyze their respective pros and cons. Then, we summarize the improvement of object recognition on the feature extraction, classifier construction, and the search framework. Finally, future research trends are proposed.