Stereo and Neural Network-based Pedestrian Detection - Robotics Institute Carnegie Mellon University

Stereo and Neural Network-based Pedestrian Detection

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

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.

BibTeX

@article{Zhao-2000-8111,
author = {Liang Zhao and Chuck Thorpe},
title = {Stereo and Neural Network-based Pedestrian Detection},
journal = {IEEE Transactions on Intelligent Transportation Systems},
year = {2000},
month = {September},
volume = {1},
number = {3},
pages = {148 -154},
}