Stereovision-based object segmentation for automotive applications

Authors:
Yingping Huang;Shan Fu;Chris Thompson
Affiliations:
International Automotive Research Center, Warwick Manufacture Group, University of Warwick, Coventry, UK;Applied Mathematics & Computing Group, School of Engineering, Cranfield University, Bedford, UK;Applied Mathematics & Computing Group, School of Engineering, Cranfield University, Bedford, UK
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2005

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Abstract

Obstacle detection and classification in a complex urban area are highly demanding, but desirable for pedestrian protection, stop & go, and enhanced parking aids. The most difficult task for the system is to segment objects from varied and complicated background. In this paper, a novel position-based object segmentation method has been proposed to solve this problem. According to the method proposed, object segmentation is performed in two steps: in depth map (X-Z plane) and in layered images (X-Y planes). The stereovision technique is used to reconstruct image points and generate the depth map. Objects are detected in the depth map. Afterwards, the original edge image is separated into different layers based on the distance of detected objects. Segmentation performed in these layered images can be easier and more reliable. It has been proved that the proposed method offers robust detection of potential obstacles and accurate measurement of their location and size.