Journal of Broadcast Engineering (방송공학회논문지)

The Korean Institute of Broadcast and Media Engineers (한국방송∙미디어공학회)
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Vehicle Headlight and Taillight Recognition in Nighttime using Low-Exposure Camera and Wavelet-based Random Forest

저노출 카메라와 웨이블릿 기반 랜덤 포레스트를 이용한 야간 자동차 전조등 및 후미등 인식

  • 허두영 (계명대학교 컴퓨터공학과) ;
  • 김상준 (계명대학교 컴퓨터공학과) ;
  • 곽충섭 (계명대학교 컴퓨터공학과) ;
  • 남재열 (계명대학교 컴퓨터공학과) ;
  • 고병철 (계명대학교 컴퓨터공학과)
  • Received : 2017.02.23
  • Accepted : 2017.04.18
  • Published : 2017.05.30
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Abstract

In this paper, we propose a novel intelligent headlight control (IHC) system which is durable to various road lights and camera movement caused by vehicle driving. For detecting candidate light blobs, the region of interest (ROI) is decided as front ROI (FROI) and back ROI (BROI) by considering the camera geometry based on perspective range estimation model. Then, light blobs such as headlights, taillights of vehicles, reflection light as well as the surrounding road lighting are segmented using two different adaptive thresholding. From the number of segmented blobs, taillights are first detected using the redness checking and random forest classifier based on Haar-like feature. For the headlight and taillight classification, we use the random forest instead of popular support vector machine or convolutional neural networks for supporting fast learning and testing in real-life applications. Pairing is performed by using the predefined geometric rules, such as vertical coordinate similarity and association check between blobs. The proposed algorithm was successfully applied to various driving sequences in night-time, and the results show that the performance of the proposed algorithms is better than that of recent related works.

본 논문에서는 차량이 움직일 때 발생하는 카메라의 움직임, 도로상의 광원에 강건한 지능형 전조등 제어 시스템을 제안한다. 후보광원을 검출할 때 카메라의 원근 범위 추정 모델을 기반으로 한 ROI (Region of Interest)를 사용하며 이는 FROI (Front ROI)와 BROI (Back ROI)로 나뉘어 사용된다. ROI내에서 차량의 전조등과 후미등, 반사광 및 주변 도로의 조명들은 2개의 적응적 임계값에 의해 세그먼트화 된다. 세그먼트화 된 광원 후보군들로부터 후미등은 적색도(redness)와 Haar-like특징에 기반한 랜덤포레스트 분류기에 의해 검출된다. 전조등과 후미등 분류 과정에서 빠른 학습과 실시간 처리를 위해 SVM(Support Vector Machine) 또는 CNN(Convolutional Neural Network)을 사용하지 않고 랜덤포레스트 분류기를 사용했다. 마지막으로 페어링(Pairing) 단계에서는 수직좌표 유사성, 광원들간의 연관성 검사와 같은 사전 정의된 규칙을 적용한다. 제안된 알고리즘은 다양한 야간 운전환경을 포함하는 데이터에 적용한 결과, 최근의 관련연구 보다 향상된 검출 성능을 보여주었다.

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References

  1. National Highway Traffic Safety Administration, "Traffic safety facts 2011: A compilation of motor vehicle crash data from the fatality analysis reporting system and the general estimates system", NHTSA Annual Report, 2011.
  2. P. F. Alcantarilla, L. M. Bergasa, P. Jimenez, M. A. Sotelo, I. Parra, D. Fernandez, "Night time vehicle detection for driving assistance lightbeam controller," IEEE Intelligent Vehicle Symposium, pp. 291-296, 2008.
  3. A. Lopez, J. Hilgenstock, A. Busse,R. Baldrich, F. Lumbreras, and J. Serrat, "Nighttime vehicle detection for intelligent headlight control," Lecture Note in Computer Science, vol. 5259, pp. 113-124, 2008.
  4. P. F. Alcantarilla, L. M. Bergasa, P. Jimenez, M. A. Sotelo, I. Parra, D. Fernandez, M. A. Sotelo, S. S. Mayoral, "Automatic lightbeam controller for driver assistance," Machine Vision and Application. vol. 22, pp. 819-835 ,2011. https://doi.org/10.1007/s00138-011-0327-y
  5. J. H. Connell, B. W. Herta, S. PanKani, H. Hess, and S. Pliefke, "A fast and robust intelligent headlight controller for vehicles," IEEE Intelligent Vehicle Symposium, pp. 703-708, 2011.
  6. W. Zhang, Q. M. J. Wu, G. Wang, and X. You,"Tracking and Pairing Vehicle Headlight in Night Scenes," IEEE Transaction on Intelligent Transportation System, vol. 13, pp. 140-153, 2011.
  7. R. O'Malley, M. Glavin, and E. Jones, "Vision-based detection and tracking of vehicles to the rear with perspective correction in low-light conditions," IET Intelligent Transportation System, vol. 5, pp. 1-10, 2011. https://doi.org/10.1049/iet-its.2010.0032
  8. D. Heo, C.-S. Kwak, S. Kim, B. C. Ko, and J. Y. Nam, "Intelligent high beam control of a vehicle for driving assistance in nighttime," International Workshop on Advanced Image Technology, pp. 1-4, 2017.
  9. S. Eum, H. G. Jung, "Enhancing light blob detection for intelligent headlight control using lane detection," IEEE Transaction on Intelligent Transportation System, vol. 14, pp. 1003-1011, 2013. https://doi.org/10.1109/TITS.2012.2233736
  10. Kamal Nasrollahi and Thomas B. Moeslund, "Haar-Like Features for Robust Real-Time Face Recognition," IEEE Int. Conf. Image Process., pp. 3073-3077, 2013.
  11. M. Jeong, B. C. Ko, and J.-Y. Nam, "Early Detection of Sudden Pedestrian Crossing for Safe Driving during Summer Nights," IEEE Trans. Circuits Syst. Video Technol., vol. 1, pp. 1-13, 2016.
  12. B. C. Ko, J.-Y. Kwak, and J.-Y. Nam, "Human tracking in thermal images using adaptive particle filters with online random forest learning," Opt. Eng. vol. 52, no. 11, pp. 113-105, 2013.
  13. S. Zhou, J.Li, Z.Shen, L.Ying "A Night time Application for a Real-Time Vehicle Detection Algorithm Based on Computer Vision" Science, Engineering and Technology vol. 5, no.10, pp. 3037-3043, 2013.
  14. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "YOLO-You only look once: Unified, real-time object detection," IEEE Conf. on Computer Vision, pp.1-10, 2016.