IEIE Transactions on Smart Processing and Computing

The Institute of Electronics and Information Engineers (대한전자공학회)
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Fast Lamp Pairing-based Vehicle Detection Robust to Atypical and Turn Signal Lamps at Night

  • Received : 2017.07.18
  • Accepted : 2017.08.07
  • Published : 2017.08.30
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Abstract

Automatic vehicle detection is a very important function for autonomous vehicles. Conventional vehicle detection approaches are based on visible-light images obtained from cameras mounted on a vehicle in the daytime. However, unlike daytime, a visible-light image is generally dark at night, and the contrast is low, which makes it difficult to recognize a vehicle. As a feature point that can be used even in the low light conditions of nighttime, the rear lamp is virtually unique. However, conventional rear lamp-based detection methods seldom cope with atypical lamps, such as LED lamps, or flashing turn signals. In this paper, we detect atypical lamps by blurring the lamp area with a low pass filter (LPF) to make out the lamp shape. We also propose to detect flickering of the turn signal lamp in a manner such that the lamp area is vertically projected, and the maximum difference of two paired lamps is examined. Experimental results show that the proposed algorithm has a higher F-measure value of 0.24 than the conventional lamp pairing-based detection methods, on average. In addition, the proposed algorithm shows a fast processing time of 6.4 ms per frame, which verifies real-time performance of the proposed algorithm.

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References

  1. W. C. Chang and C. W. Cho, "Online Boosting for Vehicle Detection," in IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 40, no. 3, pp. 892-902, June 2010. https://doi.org/10.1109/TSMCB.2009.2032527
  2. S. Sivaraman and M. M. Trivedi, "A General Active- Learning Framework for On-Road Vehicle Recognition and Tracking," in IEEE Transactions on Intelligent Transportation Systems, vol. 11, no. 2, pp. 267-276, June 2010. https://doi.org/10.1109/TITS.2010.2040177
  3. F. Han, Y. Shan, R. Cekander, H. S. Sawhney and R. Kumar, "A two-stage approach to people and vehicle detection with hog-based svm." Performance Metrics for Intelligent Systems 2006 Workshop. 2006.
  4. A. Jazayeri, H. Cai, J. Y. Zheng and M. Tuceryan, "Vehicle Detection and Tracking in Car Video Based on Motion Model," in IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 2, pp. 583-595, June 2011. https://doi.org/10.1109/TITS.2011.2113340
  5. J. Gall, A. Yao, N. Razavi, L. Van Gool and V. Lempitsky, "Hough Forests for Object Detection, Tracking, and Action Recognition," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 11, pp. 2188-2202, Nov. 2011. https://doi.org/10.1109/TPAMI.2011.70
  6. H. Kim, Y. Lee, T. Woo and H. Kim, "Integration of vehicle and lane detection for forward collision warning system," 2016 IEEE 6th International Conference on Consumer Electronics - Berlin (ICCEBerlin), Berlin, pp. 5-8, 2016.
  7. M. Taha, H. H. Zayed, T. Nazmy and M. E. Khalifa, "Multi-Vehicle Tracking Under Day and Night Illumination," International Journal of Scientific & Engineering Research, Volume 5, Issue 11, November-2014.
  8. I. Sina, A. Wibisono, A. Nurhadiyatna, B. Hardjono, W. Jatmiko and P. Mursanto, "Vehicle counting and speed measurement using headlight detection," 2013 International Conference on Advanced Computer Science and Information Systems (ICACSIS), Bali, pp. 149-154, 2013.
  9. S. Zhou, J. Shen and L. Ying, "A night time application for a real-time vehicle detection algorithm based on computer vision," Research Journal of Applied Sciences, Engineering and Technology 5.10, pp. 3037-3043, 2013. https://doi.org/10.19026/rjaset.5.4620
  10. R. O'Malley, E. Jones and M. Glavin, "Rear-Lamp Vehicle Detection and Tracking in Low-Exposure Color Video for Night Conditions," IEEE Transactions on Intelligent Transportation Systems, vol. 11, no. 2, June 2010.
  11. E. Skodras, G. Siogkas, E. Dermatas and N. Fakotakis, "Rear lights vehicle detection for collision avoidance," 2012 19th International Conference on Systems, Signals and Image Processing (IWSSIP), Vienna, pp. 134-137, April, 2012.
  12. M. Rezaei, M. Terauchi and R. Klette, "Robust Vehicle Detection and Distance Estimation Under Challenging Lighting Conditions," IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 5, October 2015.
  13. H. T. Chen, Y. C. Wu and C. C. Hsu, "Daytime Preceding Vehicle Brake Light Detection Using Monocular Vision," IEEE Sensors Journal, vol. 16, no. 1, January, 2016.
  14. R. Zhang, "Robust Pedestrian and Vehicle Detection in Night Time using Headlight Beam Pattern and Color Model," M. S. Dissertation, Inha University, February, 2013.
  15. P. Dave, N. Mounika Gella, N. Saboo and A. Das, "A novel algorithm for night time vehicle detection even with one non-functional taillight by CIOF (color inherited optical flow)," International Conference on Pattern Recognition Systems (ICPRS-16), Talca, pp. 1-6, 2016.
  16. K. M. Jeong and B. C. Song, "Night time vehicle detection using rear-lamp intensity," 2016 IEEE International Conference on Consumer Electronics- Asia (ICCE-Asia), Seoul, South Korea, pp. 1-3, 2016.
  17. J. P. Lewis, "Fast Normalized Cross-Correlation," Vision Interface, vol. 10, no. 1, pp. 120-123, 1995.
  18. D. Juric and S. Loncaric, "A method for on-road night-time vehicle headlight detection and tracking," 2014 International Conference on Connected Vehicles and Expo (ICCVE), Vienna, pp. 655-660, 2014.
  19. S. Eum and H. G. Jung, "Enhancing Light Blob Detection for Intelligent Headlight Control Using Lane Detection," in IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 2, pp. 1003-1011, June 2013. https://doi.org/10.1109/TITS.2012.2233736
  20. R. E. Kalman, "A new approach to linear filtering and prediction problems," ASME Transaction on Journal of basic Engineering, vol. 82, pp. 35-45, Mar. 1960. https://doi.org/10.1115/1.3662552
  21. G. Welch and G. Bishop, "An introduction to the Kalman filter," Dept. Comput. Sci., Univ. North Carolina, Chapel Hill, NC, Tech. Rep. TR 95-041, 2003.
  22. Powers and D. Martin. "Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation.," 2011.