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Multi-class obstacle detection and classification using stereovision and improved active contour models

Multi-class obstacle detection and classification using stereovision and improved active contour models

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Existing in-vehicle sensing systems are concentrated on obstacle detection for pedestrian or vehicle. Limited work has been conducted on multi-class obstacle classification. This study addresses on this issue and aims to develop an approach for simultaneous detection and classification of multi-class obstacles. Stereovision is first used to segment obstacles from traffic background, then an improved active contour model is adopted to extract complete contour curve of the detected obstacles. Based on the contour extracted, geometrical features including aspect ratio, area ratio and height are integrated for classifying object types including vehicle, pedestrian and other obstacles. The approach was tested on substantial urban traffic images and the corresponding results prove the effectiveness of the proposed approach.

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