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Traffic Signal Recognition with a Priori Analysis of Signal Position

  • Yingdong YuEmail author
  • Yan Lai
  • Hui Wang
  • Lan LinEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10796)

Abstract

Traffic signal recognition is one of the critical points of the technology of intelligent driving. The research on traffic signal recognition has lasted for decades and attracted more and more research attentions from the research and industry communities. Most of the previous work are based on the vision clues to deal with the problem of traffic signal recognition, while in this paper we propose a novel method which is an integration of prior signal position knowledge and the computer vision based on object detection. With prior knowledge of signal position, the detection area can be reduced and the efficiency of signal recognition is increased significantly. Experiments are conducted on the real-world dataset, and the experimental results show that compared with the previous work and the state-of-the-arts, our method achieves the best performance and satisfies the requirements of signal recognition in intelligent driving.

Keywords

Intelligent driving Traffic signal recognition Traffic signal tracking Support Vector Machine 

Notes

Acknowledgement

This work is supported by National Science Foundation of China 61373106.

References

  1. 1.
    Goetz, J., Zittlau, D., Happe, J.: Advanced driver assistance systems—enhancement of safety and comfort. Autotechnology 6(6), 34–38 (2006)Google Scholar
  2. 2.
    Lin, F., Lai, Y., Lin, L., Yuan, Y.: A traffic sign recognition method based on deep visual feature. In: 2016 Progress in Electromagnetic Research Symposium (PIERS), Shanghai, 8–11 August, pp. 2247–2250 (2016)Google Scholar
  3. 3.
    Lai, Y., Wang, N., Yang, Y., Lin, L.: Traffic signs recognition and classification based on deep feature learning. In: 7th International Conference on Pattern Recognition Applications and Methods (ICPRAM), Madeira, Portugal, 16–18 January, pp. 622–629 (2018)Google Scholar
  4. 4.
    Zhang, G., Lin, F., Lin, L.: A novel method of front vehicle recognition. In: 2016 Progress in Electromagnetic Research Symposium (PIERS), Shanghai, 8–11 August, pp. 2126–2130 (2016)Google Scholar
  5. 5.
    Park, J.H., Jeong, C.: Real-time signal light detection. In: 2nd International Conference on Future Generation Communication and Networking Symposia, pp. 139–142. IEEE (2008)Google Scholar
  6. 6.
    Omachi, M., Omachi, S.: Traffic light detection with color and edge information. In: 2nd International Conference on Computer Science and Information Technology, pp. 284–287. IEEE (2009)Google Scholar
  7. 7.
    Gong, J., Jiang, Y., Xiong, G., et al.: The recognition and tracking of traffic lights based on color segmentation and Camshift for intelligent vehicles. In: Intelligent Vehicles Symposium (IV), pp. 431–435, IEEE (2010)Google Scholar
  8. 8.
    Bradski, G.R.: Computer vision face tracking for use in a perceptual user interface. Intel Technol. J. Q2, 214–219 (1998)Google Scholar
  9. 9.
    John, V., Yoneda, K., Qi, B., et al.: Traffic light recognition in varying illumination using deep learning and saliency map. In: 17th International Conference on Intelligent Transportation Systems (ITSC), pp. 2286–2291. IEEE (2014)Google Scholar
  10. 10.
    Fairfield, N., Urmson, C.: Traffic light mapping and detection. In: International Conference on Robotics and Automation (ICRA), pp. 5421–5426. IEEE (2011)Google Scholar
  11. 11.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  12. 12.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge (2000)CrossRefGoogle Scholar
  13. 13.
    Henriques, J.F., Caseiro, R., Martins, P., et al.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2015)CrossRefGoogle Scholar
  14. 14.
    Su, C.H., Chiu, H.S., Hsieh, T.M.: An efficient image retrieval based on HSV color space. In: 2011 International Conference on Electrical and Control Engineering (ICECE), pp. 5746–5749. IEEE (2011)Google Scholar
  15. 15.
    Recky, M., Leberl, F.: Windows detection using k-means in CIE-lab color space. In: 20th International Conference on Pattern Recognition (ICPR), pp. 356–359. IEEE (2010)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electronic Science and TechnologyTongji UniversityShanghaiChina
  2. 2.School of Telecommunication Systems EngineeringTechnical University of MadridMadridSpain

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