Pedestrian detection is essential to avoid dangerous traffic situations. We present a fast and robust algorithm for detecting pedestrians in a cluttered scene from a pair of moving cameras. This is achieved through stereo-based segmentation and neural network-based recognition. The algorithm includes three steps. First, we segment the image into sub-image object candidates using disparities discontinuity. Second, we merge and split the sub-image object candidates into sub-images that satisfy pedestrian size and shape constraints. Third, we use intensity gradients of the candidate sub-images as input to a trained neural network for pedestrian recognition. The experiments on a large number of urban street scenes demonstrate that the proposed algorithm: (1) can detect pedestrians in various poses, shapes, sizes, clothing, and occlusion status; (2) runs in real-time; and (3) is robust to illumination and background changes.
Stereo- and neural network-based pedestrian detection
IEEE Transactions on Intelligent Transportation Systems ; 1 , 3 ; 148-154
2000-09-01
164993 byte
Article (Journal)
Electronic Resource
English
Stereo- and neural network-based pedestrian detection
IEEE | 1999
|Stereo- and Neural Network-Based Pedestrian Detection
British Library Online Contents | 2000
|Infrared Stereo Vision-based Pedestrian Detection
British Library Conference Proceedings | 2005
|Infrared stereo vision-based pedestrian detection
IEEE | 2005
|Stereo-based pedestrian detection and path prediction
TIBKAT | 2014
|