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

Volume

1

fYear

2009

fDate

12-15 July 2009

Firstpage

482

Lastpage

486

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/ICMLC.2009.5212506

Filename

5212506