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Carnegie Mellon Robotics Institute

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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(s) / Consortia: Vision and Autonomous Systems Center

Associated Lab(s) / Group(s): NavLab

Associated Project(s): Side Collision Warning System for Transit Buses

Text Reference

Liang Zhao and Chuck 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",
   pages = "148 -154",
   month = "September",
   year = "2000",
   volume = "1",
   number = "3",
}

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