Title :
Stereo- and neural network-based pedestrian detection
Author :
Zhao, Liang ; Thorpe, Charles E.
Author_Institution :
Robotics Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA
fDate :
9/1/2000 12:00:00 AM
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
Keywords :
driver information systems; feedforward neural nets; image segmentation; multilayer perceptrons; object detection; object recognition; stereo image processing; cluttered scene; dangerous traffic situations; disparities discontinuity; intensity gradients; moving cameras; neural network-based recognition; pedestrian detection; pedestrian recognition; stereo-based segmentation; sub-image object candidates; urban street scenes; Cameras; Image segmentation; Layout; Motion detection; Neural networks; Object detection; Real time systems; Robustness; Shape; Telecommunication traffic;
Journal_Title :
Intelligent Transportation Systems, IEEE Transactions on
DOI :
10.1109/6979.892151