Open AccessMulti‐class obstacle detection and classification using stereovision and improved active contour models
Author(s) -
Huang Yingping,
Liu Shumin
Publication year - 2016
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2014.0308
Subject(s) - obstacle , artificial intelligence , pedestrian detection , computer vision , computer science , class (philosophy) , active contour model , object detection , pedestrian , pattern recognition (psychology) , image (mathematics) , engineering , geography , image segmentation , transport engineering , archaeology
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.