Pedestrian detection using heuristic statistics and machine learning

Abstract

Pedestrian detection is an important research field in advanced driver assistance system (ADAS). This paper puts forward a pedestrian detection framework based on both heuristic statistics and machine learning. First, a restriction of region of interest (ROI) is set on the captured image. Second, the template matching coarsely detects candidate pedestrians by using a set of template images, the edge image of the current frame, and the difference image from previous and current frames. Next, the histogram analysis again roughly filters out the candidate pedestrians. Finally, Histogram of Oriented Gradients (HOG) combined with library support vector machine (LIBSVM) is used to verify those candidate pedestrians. The experimental results show that the proposed method can run in real-time, where the false negative rate is 1.43%, and the false positive rate is 0.16%.