Stereovision-Based Object Segmentation for Automotive Applications
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Stereovision-Based Object Segmentation for Automotive Applications Yingping Huang International Automotive Research Center, Warwick Manufacture Group, University of Warwick, Coventry CV4 7AL, UK Email: [email protected]
Shan Fu Applied Mathematics & Computing Group, School of Engineering, Cranfield University, Bedford MK43 0AL, UK Email: [email protected]
Chris Thompson Applied Mathematics & Computing Group, School of Engineering, Cranfield University, Bedford MK43 0AL, UK Email: [email protected] Received 14 January 2004; Revised 8 November 2004 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. Keywords and phrases: stereovision, segmentation, objects detection.
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INTRODUCTION
Vision-based driver assistance system in complex urban area is highly demanding, but desirable for pedestrian protection, stop & go, and enhanced parking aids. The basic requirement for the system is the capability of detecting potential obstacles and providing complete three-dimensional (3D) information of the obstacles, that is, size, location. Furthermore, the capability of classifying obstacles is also essential for the system to better interpret the driving environment and give correct reaction. Two basic techniques have been adopted for vision-based obstacle detection: optical flow and stereovision. The optical flow technique is equipped with monocamera, and objects are segmented according to motion pattern (e.g., optical flow vectors) by analysing two or more consecutive images taken at different time instants [1, 2, 3, 4]. Viewing a scene from two different points allows us to extract 3D structure of the scene. Franke et al. [5, 6] applied stereovision technique to interpret urban traffic scene. Bohrer et al. [7] combined
stereovision technique with inverse-perspective method to warp the left and right images so that all ground plane points have zero disparity. Simple differencing or low correlation values between identically located images points correspond to an obstacle. Bertozzi et al. [8, 9] employed the similar method in their GOLD project to detect obstacle in high way traffic. In this paradigm
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