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http://dx.doi.org/10.14372/IEMEK.2014.9.3.145

Vision based Traffic Light Detection and Recognition Methods for Daytime LED Traffic Light  

Kim, Hyun-Koo (Gyeongbuk Institute of IT Convergence Industry Technology and Yeungnam University)
Park, Ju H. (Yeungnam University)
Jung, Ho-Youl (Yeungnam University)
Publication Information
IEMEK Journal of Embedded Systems and Applications / v.9, no.3, 2014 , pp. 145-150 More about this Journal
Abstract
This paper presents an effective vision based method for LED traffic light detection at the daytime. First, the proposed method calculates horizontal coordinates to set region of interest (ROI) on input sequence images. Second, the proposed uses color segmentation method to extract region of green and red traffic light. Next, to classify traffic light and another noise, shape filter and haar-like feature value are used. Finally, temporal delay filter with weight is applied to remove blinking effect of LED traffic light, and state and weight of traffic light detection are used to classify types of traffic light. For simulations, the proposed method is implemented through Intel Core CPU with 2.80 GHz and 4 GB RAM, and tested on the urban and rural road video. Average detection rate of traffic light is 94.50 % and average recognition rate of traffic type is 90.24 %. Average computing time of the proposed method is 11 ms.
Keywords
Traffic light detection; Driving assistance system; Haar-like feature; Color segment; Weighted delay filter;
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