Stereo and Neural Network-based Pedestrian Detection

Liang Zhao and Chuck Thorpe
IEEE Transactions on Intelligent Transportation Systems, Vol. 1, No. 3, September, 2000, pp. 148 -154.

  • Adobe portable document format (pdf) (166KB)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

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.

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
   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",