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Carnegie Mellon Robotics Institute

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Abstract

In this paper, we present a real-time pedestrian detection system that uses a pair of moving cameras to detect both stationary and moving pedestrians in crowded environments. This is achieved through stereo-based segmentation and neural network-based recognition. Stereo-based segmentation allows us to extract objects from a changing background; neural network-based recognition allows us to identify pedestrians in various poses, shapes, sizes, clothing, occlusion status. The experiments on a large number of urban street scenes demonstrate the feasibility of the approach in terms of pedestrian detection rate and frame processing rate.

Keywords

Pedestrian Detection, Stereo Vision, Neural Networks, Object Recognition, Range Image Segmentation

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

Number of pages: 6

Text Reference

Liang Zhao and Chuck Thorpe, "Stereo and Neural Network-based Pedestrian Detection," Proc. 1999 Int'l Conf. on Intelligent Transportation Systems, October, 1999, pp. 298-303.

BibTeX Reference

@inproceedings{Zhao_1999_3317,
   author = "Liang Zhao and Chuck Thorpe",
   title = "Stereo and Neural Network-based Pedestrian Detection",
   booktitle = "Proc. 1999 Int'l Conf. on Intelligent Transportation Systems",
   pages = "298-303",
   month = "October",
   year = "1999",
}

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