IEMEK Journal of Embedded Systems and Applications (대한임베디드공학회논문지)

Institute of Embedded Engineering of Korea (대한임베디드공학회)
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Night-time Vehicle Detection Method Using Convolutional Neural Network

합성곱 신경망 기반 야간 차량 검출 방법

  • Received : 2017.03.02
  • Accepted : 2017.03.28
  • Published : 2017.04.30
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Abstract

In this paper, we present a night-time vehicle detection method using CNN (Convolutional Neural Network) classification. The camera based night-time vehicle detection plays an important role on various advanced driver assistance systems (ADAS) such as automatic head-lamp control system. The method consists mainly of thresholding, labeling and classification steps. The classification step is implemented by existing CIFAR-10 model CNN. Through the simulations tested on real road video, we show that CNN classification is a good alternative for night-time vehicle detection.

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References

  1. J. Rebut, B. Bradai, J. Moizard, A. Charpentierhi, "A Monocular Vision Based Advanced Lighting Automation System for Driving Assistance," Proceedings of IEEE International Symposium on Industrial Electronics, pp. 311-316, 2009.
  2. Mobileye, Adaptive Headlight Control, Available: http://www.mobileye-vision.com, http://www.mobileye.com/technology/applications/head-lamp-control.
  3. G. Elger, B. Spinger, N. Bienen, N. Benter, "LED Matrix Light Source for Adaptive Driving Beam Applications," Proceedings of IEEE Electronic Components and Technology Conference, pp. 535-540, 2013.
  4. M. Dassanayake, R. Miller, S. Abdelnour, M. S. Wrobel Jr, J. Shannon, "Adaptive Front Lighting System for a Vehicle," U.S. Patent No. 7,540,638, 2009.
  5. P.F. Alcantarilla, L.M. Bergasa, P.Jimenez, M.A. Sotelo, I. Parra, D. Fernandez, "Night Time Vehicle Detection for Driving Assistance Light Beam Controller," Proceedings of IEEE Intelligent Vehicles Symposium, pp. 291-296, 2008.
  6. V.B. Sutar1, L.S. Admuthe, "Night Time Vehicle Detection and Classification Using Support Vector Machine," IOSR Journal of VLSI and Signal Processing, Vol. 1, No. 4, pp. 1-9, 2012. https://doi.org/10.9790/4200-0140109
  7. H. Lim, H, Lee, J.H. Park, H. Jung, "Night-time Vehicle Detection Based on Multi-class SVM," IEMEK J. Embed. Sys. Appl., Vol. 10, No. 5, 2015 (in Korean).
  8. H. Wang, Y. Cai, X. Chen, L. Chen, "Night-Time Vehicle Sensing in far Infrared Image With Deep Learning," Journal of Sensors, 2016.
  9. M. Szarvas, U. Sakai, J. Ogata, "Real-time Pedestrian Detection Using LIDAR and Convolutional Neural Networks," Proceedings of IEEE Intelligent Vehicles Symposium. 2006.
  10. W. S. McCulloch, W. Pitts, "A Logical Calculus of the Ideas Immanent in Nervous Activity," The bulletin of mathematical biophysics Vol. 5, No. 4, pp. 115-133, 1943. https://doi.org/10.1007/BF02478259
  11. Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, "Gradient-based Learning Applied to Document Recognition," Proceedings of the IEEE Vol. 86, No. 11, pp. 2278-2324, 1998. https://doi.org/10.1109/5.726791
  12. A. Krizhevsky, I. Sutskever, G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Advances in neural information processing systems, pp. 1097-1105, 2012.
  13. R. Girshick, J. Donahue, T. Darrell, J. Malik, "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation," Proceedings of IEEE conference on computer vision and pattern recognition pp. 580-587.
  14. S. Ren, K. He, R. Girshick, J. Sun, "Faster r-cnn: Towards Real-time Object Detection With Region Proposal Networks," In Advances in neural information processing systems, pp. 91-99, 2015.
  15. A. Krizhevsky, G. Hinton. "Convolutional Deep Belief Networks on cifar-10," Unpublished manuscript 40, 2010.