Title :
Unstructured road detection using hybrid features
Author :
Wang, Jian ; Ji, Zhong ; Su, Yu-ting
Author_Institution :
Sch. of Electron. Inf. Eng., Tianjin Univ., Tianjin, China
Abstract :
Road detection is a key step of the autonomous guided vehicle system such as road following. In this paper, a novel unstructured road detection method is proposed. First, white balance and gray level stretch technique are adopted to enhance image performance. Then, a small overlapped sliding window is scanned over the frame from which hybrid features are extracted. Next, a SVM-based classifier is employed to distinguish the road area from background. At last, the morphological operation and moving average filter technology are performed to obtain precise location of the road region. The proposed algorithm has been evaluated by different type of unstructured roads and the experimental results show its effectiveness.
Keywords :
image classification; image enhancement; road traffic; support vector machines; SVM-based classifier; autonomous guided vehicle system; gray level stretch technique; hybrid features; image performance enhancement; road following; support vector machines-based classifier; unstructured road detection; white balance technique; Computer vision; Cybernetics; Feature extraction; Machine learning; Mobile robots; Remotely operated vehicles; Roads; Shape; Support vector machines; Vehicle detection; Autonomous guided vehicle; Hybrid features; SVM; Unstructured road detection;
Conference_Titel :
Machine Learning and Cybernetics, 2009 International Conference on
Conference_Location :
Baoding
Print_ISBN :
978-1-4244-3702-3
Electronic_ISBN :
978-1-4244-3703-0
DOI :
10.1109/ICMLC.2009.5212506
