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RI | Publications | Stereo and Neural Network-based Pedestrian Detection
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Stereo and Neural Network-based Pedestrian Detection
L. Zhao and C. Thorpe
IEEE Transactions on Intelligent Transportation Systems, Vol. 1, No. 3, September, 2000, pp. 148 -154.
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| Abstract |
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.
| Notes |
Associated center: VASC
Associated lab/group: NavLab
Associated project: Side Collision Warning System for Transit Buses
| Text Reference |
L. Zhao and C. Thorpe, "Stereo and Neural Network-based Pedestrian Detection," IEEE Transactions on Intelligent Transportation Systems, Vol. 1, No. 3, September, 2000, pp. 148 -154.
| BibTeX Reference |
@article{Zhao_2000_3865,
author = "Liang Zhao and Chuck Thorpe",
title = "Stereo and Neural Network-based Pedestrian Detection",
journal = "IEEE Transactions on Intelligent Transportation Systems",
month = "September",
year = "2000",
volume = "1",
number = "3",
pages = "148 -154"
}