Pedestrian detection is a research hotspot and a difficult issue in the computer vision such as the Intelligent Surveillance System, the Intelligent Transport System, robotics, and automotive safety. However, the human body's position, angle, and dress in a video scene are complicated and changeable, which have a great influence on the detection accuracy. In this paper, through the analysis on the pros and cons of Census Transform Histogram (CENTRIST), a novel feature is presented for human detection Ternary CENTRIST (T-CENTRIST). The T-CENTRIST feature takes the relationship between each pixel and its neighborhood pixels into account. Meanwhile, it also considers the relevancy among these neighborhood pixels. Therefore, the proposed feature description method can reflect the silhouette of pedestrian more adequately and accurately than that of CENTRIST. Second, we propose a fast pedestrian detection framework based on T-CENTRIST in infrared image, which introduces the idea of extended blocks and the integral image. Finally, experimental results verify the effectiveness of the proposed pedestrian detection method.
翻译：Pedestrian探测是一个研究热点,是计算机视野中的一个难题,如智能监测系统、智能运输系统、机器人和汽车安全。然而,人体在视频场景中的位置、角度和着装是复杂和可变的,对探测准确性有很大影响。在本文中,通过对普查转换历史图的利弊的分析,为人类探测Ternary CentriST(T-CENTRISST)提出了一个新特点。T-CENTRIST的特征反映了每个像素及其相邻像素之间的关系。与此同时,它也考虑到这些相邻像素之间的相关性。因此,拟议的特征描述方法可以比CentRIST更充分和准确地反映行人的声音。第二,我们提议一个基于T-CENTRIT的红外图像快速行人行人探测框架,其中介绍了扩展区块和综合图像的概念。最后,实验结果可以核实拟议的行人探测方法的有效性。
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