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http://dx.doi.org/10.5909/JBE.2017.22.1.62

Real Time Traffic Light Detection Algorithm Based on Color Map and Multilayer HOG-SVM  

Kim, Sanggi (Kyungpook National University, Graduate School of Electronics Engineering)
Han, Dong Seog (Kyungpook National University, School of Electronics Engineering, College of IT Engineering)
Publication Information
Journal of Broadcast Engineering / v.22, no.1, 2017 , pp. 62-69 More about this Journal
Abstract
Accurate detection of traffic lights is very important for the advanced driver assistance system (ADAS). There have been many research developments in this area. However, conventional of image processing methods are usually sensitive to varying illumination conditions. This paper proposes a traffic light detection algorithm to overcome this situation. The proposed algorithm first detects the candidates of traffic light using the proposed color map and hue-saturation-value (HSV) Traffic lights are then detected using the conventional histogram of oriented gradients (HOG) descriptor and support vector machine (SVM). Finally, the proposed Multilayer HOG descriptor is used to determine the direction information indicated by traffic lights. The proposed algorithm shows a high detection rate in real-time.
Keywords
traffic light detection (TLD); support vector machine (SVM); histogram of oriented gradient (HOG); advanced driver assistance system (ADAS);
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1 L. Itti, C. Koch, and E. Niebur, "A model of saliency-based visual attention for rapid scene analysis," IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 11, pp. 1254-1259, Nov. 1998.   DOI
2 J. Levinson, J. Askeland, J. Dolson and S. Thrun, "Traffic light mapping, localization and state detection for autonomous vehicles," in proc. IEEE International Conference on Robotics and Automation, Shanghai, pp. 5784-5791, May. 2011
3 N. Fairfield and C. Urmson, "Traffic light mapping and detection," in proc. IEEE International Conference on Robotics and Automation, Shanghai, pp. 5421-5426, 2011.
4 V. John, K. Yoneda, B. Qi, Z. Liu, and S. Mita, "Traffic light recognition in varying illumination using deep learning and saliency map," in proc. IEEE International Conference on Intelligent Transportation Systems, Qingdao, pp. 2286-2291, Oct. 2014
5 R. de Charette and F. Nashashibi, "Real time visual traffic lights recognition based on spot light detection and adaptive traffic lights templates," in proc. IEEE Intelligent Vehicles Symposium, Xi'an, pp. 358-363, Jun. 2009.
6 R. de Charette and F. Nashashibi. "Traffic light recognition using image processing compared to learning processes," in proc. IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, pp. 333-338, Oct. 2009.
7 G. Siogkas, E.Skodras and E. Dermatas. "Traffic lights detection in adverse conditions using color symmetry and spatiotemporal information," in proc. International Conference on Computer Vision Theory and Applications, 2012.
8 Q. Chen, Z. Shi, Z. Zou, "Robust and real-time traffic light recognition based on hierarchical vision architecture," in proc. IEEE International Congress on Image and Signal Processing, Dalian, pp. 114-119, Oct. 2014.
9 T. Hwang, I. Joo, and S. Cho, "Detection of traffic lights for vision-based car navigation systems," in proc. Advances in Image and Video Technology, First Pacific Rim Symposium, Taiwan, pp. 682-691, Dec. 2006.
10 Y. Shen, U. Ozguner, K. Redmill, "A robust video based traffic light detection algorithm for intelligent vehicles," in proc. IEEE Intelligent Vehicles Symposium, Xi'an, pp. 521-526, Jun. 2009.
11 V. John, K. Yoneda, B. Qi, Z. Liu, and S. Mita, "Saliency map generation by the convolutional neural network for real-time traffic light detection using template matching," IEEE Transaction on Computational Imaging, vol. 1, no. 3, September. 2015
12 U. Laboratory for Intelligent and Safe Automobiles . (2015) Vision for intelligent vehicles and applications (viva) challenge. [Online]. Available: http://cvrr.ucsd.edu/vivachallenge/
13 Robotics Centre of Mines ParisTech. (2015) Traffic lights recognition (TLR) public benchmarks. [Online]. Available: http://www.lara.prd.fr/benchmarks/trafficlightsrecognition