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 show that the detection time is basically unchanged

and the detection accuracy have been improved.