Recognition algorithm for turn light of front vehicle

Abstract

Intelligent vehicle needs the turn light information of front vehicles to make decisions in autonomous navigation. A recognition algorithm was designed to get information of turn light. Approximated center segmentation method was designed to divide the front vehicle image into two parts by using geometry information. The number of remained pixels of vehicle image which was filtered by the morphologic features was got by adaptive threshold method, and it was applied to recognizing the lights flashing. The experimental results show that the algorithm can not only distinguish the two turn lights of vehicle but also recognize the information of them. The algorithm is quiet effective, robust and satisfactory in real-time performance.

Keywords

intelligent vehicle / turn light recognition / adaptive threshold / front vehicle

References

[1] JonesW.. Keeping cars from crashing [J]. IEEE Spectrum, 2001, 38(9): 40-45

[2] XuY.-c., WangH.-b., LiB.. A summary of worldwide intelligent vehicle [J]. Automotive Engineering, 2001, 23(5): 289-295

[3] XuY.-ang.Research on license plate location and tail lamp area extraction methods [D], 2008, Xi’an, Xidian University

[4] TongJ.-j., ZouF.-ming.. Speed measurement of vehicle by video image [J]. Journal of Image and Graphics, 2005, 10(2): 192-196

[5] WU Bing-fei, CHEN Yen-lin. Real-time image segmentation and rule-based reasoning for vehicle head light detection on a moving vehicle [C]// 7th IASTED International Conference on Signal & Image Processing. Anaheim: ACTA, 2005: 388–393.

[6] EichnerM. L., BreckonT. P.. Real-time video analysis for vehicle lights detection using temporal information [C]. 4th European Conference on Visual Media Production (CVMP 2007), 2007, London, EIT: 1-1

[7] TecnicoI. S., de TelecomunI., PortugalL.. Car recognition based on back lights and rear view features [C]. 10th Workshop on Image Analysis for Multimedia Interactive Services, 2009, London, IEEE: 137-140

[8] YuY.-tao.Vehicle turn light recognition based vision [D], 2006, Kunming, Yunnan Normal University

[9] BertozziM., BroggiA., CellarioM.. Artificial vision in road vehicles [J]. IEEE-Special issue on Technology and Touls for Visual Perception, 2002, 90(7): 1258-1271

[10] ZhangH.-l., ZouZ., LiJ., ChenX.-tao.. Flame image recognition of alumina rotary kiln by artificial neural network and support vector machine methods [J]. Journal of Central South University of Technology, 2008, 15(1): 39-43

[11] CaiZ.-x., HeH.-g., ChenH.Theories and methods for mobile robots navigation under unknown environments [M], 2009, Beijing, Science Press: 25-46

[12] ZhangG., MaZ.-m., DengL.-g., XuC.-min.. Novel histogram descriptor for global feature extraction and description [J]. Journal of Central South University of Technology, 2010, 17(3): 580-586

[13] SunZ.-h., BebisG., MillerR.. Monocular precrash vehicle detection: Features and classifiers [J]. IEEE Transactions on Image Processing, 2006, 15(7): 2019-2034

[14] GonzalezDigital image processing [M], 20082nd Ed.Beijing, Publishing House of Electronics Industry: 237-238

[15] YU Shi-qi. OpenCv reference manual [EB/OL]. [2010-03-10]. http://www.opencv.org.cn/index.php/Template:Doc