AdaBoost pedestrian detection algorithm based on dual-threshold motion area segmentation
基于双阈值运动区域分割的AdaBoost行人检测算法

In view of the problem that the pedestrian detection in the video sequences from a monocular fixed camera was easily effected by the noise with background difference method or the detection speed was low with the AdaBoost algorithm respectively, this paper proposed a fast AdaBoost pedestrian detection algorithm based on dual-threshold motion area segmentation by combining AdaBoost with background difference. First, it setup background frame and the foreground frame subtracted it to get the differential image. It extracted the two thresholds between the fitted Gauss noise and the bright/dark motion target from the differential image, and then segmented out the moving areas. Second, it selected a small amount of available Haar-like weak rectangle features to integrate a strong classifier by the AdaBoost learning algorithm. Finally, it adopted strong classifier to judge if the moving areas included the pedestrians. Experimental results show that the algorithm not only reduces the detection range compared to the whole image range and accelerates detection, but also decreases the false alarm rate.