DocumentCode
456783
Title
Multiple Probabilistic Templates Based Pedestrian Detection in Night Driving with a Normal Camera
Author
Hu, Mei
Author_Institution
Dept. of Autom., Tsinghua Univ., Beijing
Volume
2
fYear
2006
fDate
Aug. 30 2006-Sept. 1 2006
Firstpage
574
Lastpage
577
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Innovative Computing, Information and Control, 2006. ICICIC '06. First International Conference on
Conference_Location
Beijing
Print_ISBN
0-7695-2616-0
Type
conf
DOI
10.1109/ICICIC.2006.315
Filename
1692052
Link To Document