@article{(Open Science Index):https://publications.waset.org/pdf/8944,
	  title     = {Night-Time Traffic Light Detection Based On SVM with Geometric Moment Features},
	  author    = {Hyun-Koo Kim and  Young-Nam Shin and  Sa-gong Kuk and  Ju H. Park and  Ho-Youl Jung},
	  country	= {},
	  institution	= {},
	  abstract     = {This paper presents an effective traffic lights detection
method at the night-time. First, candidate blobs of traffic lights are
extracted from RGB color image. Input image is represented on the
dominant color domain by using color transform proposed by Ruta,
then red and green color dominant regions are selected as candidates.
After candidate blob selection, we carry out shape filter for noise
reduction using information of blobs such as length, area, area of
boundary box, etc. A multi-class classifier based on SVM (Support
Vector Machine) applies into the candidates. Three kinds of features
are used. We use basic features such as blob width, height, center
coordinate, area, area of blob. Bright based stochastic features are also
used. In particular, geometric based moment-s values between
candidate region and adjacent region are proposed and used to improve
the detection performance. The proposed system is implemented on
Intel Core CPU with 2.80 GHz and 4 GB RAM and tested with the
urban and rural road videos. Through the test, we show that the
proposed method using PF, BMF, and GMF reaches up to 93 % of
detection rate with computation time of in average 15 ms/frame.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {7},
	  number    = {4},
	  year      = {2013},
	  pages     = {472 - 475},
	  ee        = {https://publications.waset.org/pdf/8944},
	  url   	= {https://publications.waset.org/vol/76},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 76, 2013},
	}