中文摘要：

车载红外行人检测在准确率和实时性方面存在多方挑战.文中基于行人头部、躯干成像与背景之间存在灰度分布差异,构建行人头部模型和躯干模型作为前端分类器,后端采用支持向量机（Support Vector Machine,SVM）进行分类;结合多帧校验和最近邻匹配跟踪行人.实验结果表明,检测时间基本持平,提高了检测准确率.

英文摘要：

There are lots of challenges in terms of precision and real-time performance in the detection of vehicular infrared pedestrian. This article established the pedestrians＇ head and torso models as the frond-end classifiers based on the brightness distribution difference between the pedestrians＇ head,torso and the background,and adopted the support vector machine（ SVM） as the rear-end classifier; multi-frame check and nearest matching were combined to track the pedestrians.Experiment results showthat the detection time is basically unchanged,and the detection accuracy have been improved.