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Stereo- and neural network-based pedestrian detection

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2 Author(s)
Liang Zhao ; Robotics Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA ; Thorpe, C.E.

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

Published in:

Intelligent Transportation Systems, IEEE Transactions on  (Volume:1 ,  Issue: 3 )