INTEGRATION OF GENERATIVE LEARNING AND MULTIPLE POSE CLASSIFIERS FOR PEDESTRIAN DETECTION
Hidefumi Yoshida, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Kunihiro Goto, Yoshikatsu Kimura, Takashi Naito · 2012
Abstract: Recently, pedestrian detection from in-vehicle camera images is becoming an important technology in ITS (Intelligent Transportation System). However, it is difficult to detect pedestrians stably due to the variety of their poses and their backgrounds. To tackle this problem, we propose a method to detect various pedestrians from in-vehicle camera images by using multiple classifiers corresponding to various pedestrian pose classes. Since pedestrians ’ pose varies widely, it is difficult to construct a single classifier that can detect pedestrians with various poses stably. Therefore, this paper constructs multiple classifiers optimized for variously posed pedestrians by classifying pedestrian images into multiple pose classes. Also, to reduce the bias and the cost for preparing numerous pedestrian images for each pose class for learning, the proposed method employs a generative learning method. Finally, the proposed method constructs multiple classifiers by using the syn-thesized pedestrian images. Experimental results showed that the detection accuracy of the proposed method outperformed comparative methods, and we confirmed that the proposed method could detect variously posed pedestrians stably. 1