Title :
Multiple Probabilistic Templates Based Pedestrian Detection in Night Driving with a Normal Camera
Author_Institution :
Dept. of Autom., Tsinghua Univ., Beijing
fDate :
Aug. 30 2006-Sept. 1 2006
Abstract :
Pedestrian detection is particularly challenging, comparing with other targets in the domain of object detection, especially for night driving just with a normal camera. In this paper we combine two probabilistic templates based classifiers for elaborate pedestrian detection: the binary probabilistic template based classifier (BPTC) as the first layer to reject most of non-pedestrians by the features of binary image; the gray probabilistic template based classifier (GPTC) as the second layer to make the final classification by the gray probability, which is the contribution of this paper. Experiments show that our approach performs well most of the time, and the system can achieve real-time detection
Keywords :
automated highways; cameras; feature extraction; image classification; image segmentation; object detection; probability; feature extraction; image classification; multiple probabilistic template based classifier; night driving; object detection; pedestrian detection; Adaptive filters; Automation; Cameras; Computer crashes; Image edge detection; Injuries; Intelligent transportation systems; Object detection; Road accidents; Shape;
Conference_Titel :
Innovative Computing, Information and Control, 2006. ICICIC '06. First International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7695-2616-0
DOI :
10.1109/ICICIC.2006.315