DocumentCode :
2535456
Title :
A robust video based traffic light detection algorithm for intelligent vehicles
Author :
Shen, Yehu ; Ozguner, Umit ; Redmill, Keith ; Liu, Jilin
Author_Institution :
Dept. of Electr. & Comput. Eng., Ohio State Univ., Columbus, OH, USA
fYear :
2009
fDate :
3-5 June 2009
Firstpage :
521
Lastpage :
526
Abstract :
Recently, researches on intelligent vehicles which can drive in urban environment autonomously become more popular. Traffic lights are common in cities and are important cues for the path planning of intelligent vehicles. In this paper, a robust and efficient algorithm to detect traffic lights based on video sequences captured by a low cost off-the-shelf video camera is proposed. The algorithm models the hue and saturation according to Gaussian distributions and learns their parameters with training images. From learned models, candidate regions of the traffic lights in the test images can be extracted. Post processing method which takes account of the shape information is applied to the candidate regions. Furthermore, detection results of the previous image frames are aggregated in order to provide a more robust result. Experimental results on several video sequences captured in typical urban environment prove the effectiveness of the proposed algorithm.
Keywords :
Gaussian distribution; automated highways; image sequences; object detection; path planning; video signal processing; Gaussian distributions; image frames; intelligent vehicles; off-the-shelf video camera; path planning; post processing method; robust video based traffic light detection algorithm; shape information; training images; urban environment; video sequences; Cameras; Cities and towns; Costs; Detection algorithms; Gaussian distribution; Intelligent vehicles; Path planning; Robustness; Traffic control; Video sequences;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Vehicles Symposium, 2009 IEEE
Conference_Location :
Xi´an
ISSN :
1931-0587
Print_ISBN :
978-1-4244-3503-6
Electronic_ISBN :
1931-0587
Type :
conf
DOI :
10.1109/IVS.2009.5164332
Filename :
5164332
Link To Document :
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