Self-adaptive road detection method based on vision and cluster analysis

Author

Jiajie Yao ; Shiyuan Lu ; Gangfeng Yan

Author_Institution

Asus Intell. Syst. Lab., Zhejiang Univ., Hangzhou, China

fYear

2013

fDate

23-24 Dec. 2013

Firstpage

471

Lastpage

476

Abstract

A self-adaptive road detection method based on vision and cluster analysis is proposed for automatic guided vehicles. Based on the K-means algorithm, an automatic sample selection method is developed - while moving, the vehicle automatically selects new road samples and takes cluster analysis at a specified time interval to get the latest road features. Verified by experiments on campus roads, the proposed method is adaptive to the changes of road conditions. The influence of illumination, shadow, and road texture to the detection results is effectively reduced. Much less manual operations are needed compared to the traditional approaches based on learning algorithms.

Keywords

automatic guided vehicles; edge detection; feature extraction; learning (artificial intelligence); pattern clustering; roads; robot vision; statistical analysis; traffic information systems; K-means algorithm; automatic guided vehicles; automatic road sample selection method; campus roads; cluster analysis; illumination; learning algorithms; road conditions; road features; road texture; self-adaptive road detection method; shadow; vision analysis; Classification algorithms; Clustering algorithms; Feature extraction; Image color analysis; Image edge detection; Roads; Vehicles; automatic sample selection; cluster analysis; road detection;

fLanguage

English

Publisher

ieee

Conference_Titel

Instrumentation and Measurement, Sensor Network and Automation (IMSNA), 2013 2nd International Symposium on

Conference_Location

Toronto, ON

Type

conf

DOI

10.1109/IMSNA.2013.6743318

Filename

6743318