Robust and Real-Time Traffic Lights Recognition in Complex Urban Environments

References

• Wang , W. , Hou , F. and Tan , H. 2010 . A Framework for Function Allocation in Intelligent Driver Interface Design for Comfort and Safety . International Journal of Computational Intelligence Systems , 3 ( 5 ) : 531 – 541 .
   Web of Science ®Google Scholar
• Wang , W. , Guo , H. and Ikeuchi , K. 2011 . Numerical simulation and analysis procedure for digital driving dependability in intelligent transport system . KSCE Journal of Civil Engineering , 15 ( 5 ) : 891 – 898 .
   Web of Science ®Google Scholar
• Wang , W. , Mao , Y. and Jing , J. 2011 . Driver's various information process and multi-ruled decision-making mechanism: a fundamental of intelligent driving shaping model . International Journal of Computational Intelligence Systems , 4 ( 3 ) : 297 – 305 .
   Web of Science ®Google Scholar
• Hwang , T. , Joo , I. and Cho , S. 2006 . “ Detection of traffic lights for vision-based car navigation systems ” . In Pacific-Rim Symposium on Image and Video Technology 682 – 691 .
   Google Scholar
• Escalera , A. , Armingol , J. and Mata , M. 2003 . Traffic sign recognition and analysis for intelligent vehicles . Image and Vision Computing , 21 : 247 – 258 .
   Web of Science ®Google Scholar
• Soetedjo , A. and Yamada , K. Fast and robust traffic sign detection . IEEE International Conference on Systems, Man and Cybernetics . pp. 1341 – 1346 .
   Google Scholar
• Gao , X. W. , Podladchikova , L. and Shaposhnikov , D. 2005 . Recognition of traffic signs based on their colour and shape features extracted using human vision models . Journal of Visual Communication and Image Representation , : 675 – 685 .
   Web of Science ®Google Scholar
• Lu , K. , Wang , C. and Chen , S. 2008 . Traffic light recognition [J] . Journal of the Chinese Institute of Engineers , 31 ( 6 ) : 1069 – 1075 .
   Web of Science ®Google Scholar
• Gong , J. , Jiang , Y. and Xiong , G. 2010 . “ The recognition and tracking of traffic lights based on color segmentation and CAMSHIFT for intelligent vehicles ” . In IEEE Intelligent Vehicles Symposium , 431 – 435 . San Diego, CA , , USA : University of California .
   Google Scholar
• Omachi , M. and Omachi , S. 2009 . Traffic light detection with color and edge information . Computer Science and Information Technology , : 284 – 287 .
   Google Scholar
• Omachi , M. and Omachi , S. Detection of traffic light using structural information . The IEEE International Conference on Signal Processing (ICSP) . pp. 809 – 812 .
   Google Scholar
• Lindner , F. , Kressel , U. and Kaelberer , S. 2004 . “ Robust recognition of traffic signals ” . In IEEE Intelligent Vehicles Symposium [C] 49 – 53 . Piscataway, NJ , , USA
   Google Scholar
• Chung , Y. , Wang , I. and Chen , S. 2002 . A vision-based traffic light detection system at intersections . Journal of National Taiwan Normal University: Mathematics, Science & Technology , 47 : 67 – 86 .
   Google Scholar
• Yung , N. and Lai , A. 2001 . An effective video analysis method for detecting red light runners . IEEE Trans. Vehicular Technology , 50 : 1074 – 1084 .
   Web of Science ®Google Scholar
• Kim , Y. K. , Kim , K. W. and Yang , X. Real time traffic light recognition system for color vision deficiencies . IEEE International Conference on Mechatronics and Automation . pp. 76 – 81 . Washington , DC
   Google Scholar
• Shen , Y. , Ozguner , U. and Redmill , K. 2009 . “ A robust video based traffic light detection algorithm for intelligent vehicles ” . In IEEE Intelligent Vehicles Symposium [C] 521 – 526 . Piscataway, NJ , , USA
   Google Scholar
• Charette , R. d. and Nashashibi , F. Traffic light recognition using image processing compared to learning processes . The 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems . pp. 333 – 338 . St. Louis , , USA
   Google Scholar
• Charette , R. d. and Nashashibi , F. 2009 . “ Real time visual traffic lights recognition based on spot light detection and adaptive traffic lights templates ” . In IEEE Intelligent Vehicles Symposium [C] 358 – 363 . Piscataway, NJ , , USA
   Google Scholar
• Gonzalez , R. and Woods , R. 1992 . Digital Image Processing , Addison-Wesley .
   Google Scholar
• Chang , Y. and Li , X. 1994 . Adaptive image region-growing . IEEE Transactions on Image Processing , : 868 – 872 .
   PubMed Web of Science ®Google Scholar
• Lewis , J. P. Fast normalized cross-correlation , Industrial Light and Magic .
   Google Scholar