中文摘要：

辅助驾驶系统需要实时而准确的行人检测方法．文中利用基于知识的方法复杂度小的优点，针对单目远红外视频数据，提出一种基于概率模板匹配的夜间行人检测方法．该方法基于行人样本的灰度分布特征，采用局部双闽值分割算法提取候选目标，进而根据行人的运动方向建立多尺度概率模板，对候选目标进行判别．该概率模板建立方式缓解了行人样本类内方差较大的问题，增强了概率模板归纳行人外观模式的能力．为改善行人检测的准确度，进一步将目标跟踪算法融入概率模板匹配，借助多帧的综合处理结果实现了更为鲁棒的目标归属判断．实验结果表明：该方法计算开销较低，实时性较好；在郊外场景中检测率不低于90％，虚警率不高于10％；而在市区场景中检测率约为75％，虚警率约为22％．

英文摘要：

In driver assistance systems, real-time and accurate pedestrian detection is required. In this paper, by taking advantage of the low complexity of a knowledge-based detection method, a nighttime pedestrian detection method for monocular far-infrared video data is proposed based on the probabilistic template matching. In this me- thod, according to the distribution of intensity in pedestrian samples, a local dual threshold segmentation algorithm is adopted to extract the candidate regions that may contain pedestrians. Then, multi-scale probabilistic templates are established based on the moving directions of pedestrian samples and are employed to recognize the potential pe- destrians from the candidate regions. The establishment mode of probabilistic templates alleviates the large within- class variability of pedestrian samples, thus improving the induction abilities of the probabilistic templates for the appearance of pedestrians. In order to further improve the detection accuracy, the probabilistic template matching is integrated with the object-tracking algorithm, which results in more robust final decision through the multi-frame validation. Experimental results show that the proposed method can realize a real-time pedestrian detection with a low computation cost; and that it achieves a detection rate of more than 90% at the false alarm rate of less than 10% on suburban scenes while a detection rate of about 75% at the false alarm rate of about 22% on urban scenes.