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

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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.

Associated center: VASC
Associated lab/group: NavLab
Associated project: Side Collision Warning System for Transit Buses

Number of pages: 6

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

@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",
   month = "October",
   year = "1999",
   pages = "298-303"
}