한국멀티미디어학회논문지 (Journal of Korea Multimedia Society)

  • 제20권12호
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  • Pages.1901-1912
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  • 2017
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  • 1229-7771(pISSN)
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  • 2384-0102(eISSN)
한국멀티미디어학회 (Korea Multimedia Society)
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야간 영상에서의 빛 번짐 현상을 이용한 교통신호등 인식

Traffic Light Recognition Based on the Glow Effect at Night Image

  • Kim, Min-Ki (Dept. of Computer Science in Gyeongsang National Univ. Engineering Research Institute)
  • 투고 : 2017.10.19
  • 심사 : 2017.12.14
  • 발행 : 2017.12.31
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초록

Traffic lights at night are usually framed in the image as bright regions bigger than the real size due to glow effect. Moreover, the colors of lighting region saturate to white. So it is difficult to distinguish between different traffic lights at night. Many related studies have tried to decrease the glow effect in the process of capturing images. Some studies drastically decreased the shutter time of the camera to reduce the adverse effect by the glow. However, this makes the video too dark. This study proposes a new idea which utilizes the glow effect. It examines the outer radial region of traffic light. It presents an algorithm to discriminate the color of traffic light by the analysis of the outer radial region. The advantage of the proposed method is that it can recognize traffic lights in the image captured by an ordinary black box camera. Experimental results using seven short videos show the performance of traffic light recognition reporting the precision of 96.4% and the recall of 98.2%. These results show that the proposed method is valid and effective.

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참고문헌

  1. G. Siogkas, E. Skodras, and E. Dermatas, "Traffic Lights Detection in Adverse Conditions Using Color, Symmetry and Spatiotemporal Information," Proceedings of the International Conference on Computer Vision Theory and Applications, pp. 620-627, 2012.
  2. M. Diaz-Cabrera and P. Cerri, "Traffic Light Recognition During the Night Based on Fuzzy Logic Clustering," Proceedings of International Conference on Computer Aided Systems Theory, Vol. 8112, pp. 93-100, 2013.
  3. J. Kim and J. Kim, “Performance Improvement of Traffic Signal Lights Recognition Based on Adaptive Morphological Analysis,” Journal of the Korea Institute of Information and Communication Engineering, Vol. 19, No. 9, pp. 2129-2137, 2015. https://doi.org/10.6109/jkiice.2015.19.9.2129
  4. M. Kim, “Detection of a Light Region Based on Intensity and Saturation and Traffic Light Discrimination by Model Verification,” Journal of Korea Multimedia Society, Vol. 20, No. 11, pp. 1729-1740, 2017. https://doi.org/10.9717/KMMS.2017.20.11.1729
  5. Z. Cai, Y. Li, and M. Gu, "Real-time Recognition System of Traffic Light in Urban Environment," Proceedings of the 2012 IEEE Symposium on Computational Intelligence for Security and Defense Applications, pp. 1-6, 2012.
  6. M.D. Cabrera, P. Cerri, and J.S. Medina, "Suspended Traffic Lights Detection and Distance Estimation Using Color Features," Proceedings of the IEEE International Conference on Intelligent Transportation Systems, pp. 1315-1320, 2012.
  7. B. Fan, W. Lin, and X. Yang, "An Efficient Framework for Recognizing Traffic Lights in Night Traffic Images," Proceedings of the 5th International Congress on Image and Signal Processing, pp. 832-835, 2012.
  8. H. Kim, Y. Shin, S. Kuk, J. Park, and H. Jung, "Night-Time Traffic Light Detection Based on SVM with Geometric Moment Features," Journal of the International Scholarly and Scientific Research and Innovation, Vol. 7, No. 4, pp. 472-475, 2013.
  9. A. Ruta, Y. Li, and X. Liu, "Real-Time Traffic Sign Recognition from Video by Class-Specific Discriminative Features," Pattern Recognition, Vol. 43, pp. 416-430, 2010. https://doi.org/10.1016/j.patcog.2009.05.018
  10. M.B. Jensen, M.P. Philipsen, C. Bahnsen, A. Mogelmose, T.B. Moselund, and M.M. Trivedi, "Traffic Light Detection at Night: Comparison of a Learning-Based Detector and Three Model-Based Detectors," Proceedings of International Symposium on Advances in Visual Computing, Vol. 9474, pp. 774-783, 2015.
  11. M. Diaz-Cabrera, P. Cerri, and P. Medici, “Rubust Real-Time Traffic Light Detection and Distance Estimation Using a Single Camera,” Expert Systems with Applications, Vol. 42, No. 8, pp. 3911-3923, 2015. https://doi.org/10.1016/j.eswa.2014.12.037
  12. V. Haltakov, J. Mayr, C. Unger, and S. Ilic, "Semantic Segmentation Based on Traffic Light Detection at Day and at Night," Proceedings of German Conference on Pattern Recognition, Vol. 9358, pp. 446-457, 2015.
  13. J. Kim, “Traffic Lights Detection Based on Visual Attention and Spot-Lights Regions Detection,” Journal of the Institute of Electronics and Information Engineers, Vol. 5, No. 6, pp. 1260-1270, 2014.
  14. V. John, K. Yoneda, Z. Liu, and S. Mita, “Saliency Map Generation by the Convolutional Neural Network for Real-TIme Traffic Light Detection Using Template Matching,” IEEE Transactions on Computational Imaging, Vol. 1, No. 3, pp. 159-173, 2015. https://doi.org/10.1109/TCI.2015.2480006