Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Camera and LIDAR Combined System for On-Road Vehicle Detection

도로 상의 자동차 탐지를 위한 카메라와 LIDAR 복합 시스템

  • 황재필 (연세대학교 전기전자공학부) ;
  • 박성근 (연세대학교 전기전자공학부) ;
  • 김은태 (연세대학교 전기전자공학부) ;
  • 강형진 (주) 만도 중앙연구소)
  • Published : 2009.04.01
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we design an on-road vehicle detection system based on the combination of a camera and a LIDAR system. In the proposed system, the candidate area is selected from the LIDAR data using a grouping algorithm. Then, the selected candidate area is scanned by an SVM to find an actual vehicle. The morphological edged images are used as features in a camera. The principal components of the edged images called eigencar are employed to train the SVM. We conducted experiments to show that the on-road vehicle detection system developed in this paper demonstrates about 80% accuracy and runs with 20 scans per second on LIDAR and 10 frames per second on camera.

Keywords

References

  1. M. Bertozzi, A. Broggi, G Conte, and A. Fascioli, 'Obstacle and lane detection on ARGO,' IEEE Intelligent Transportation Systems Conf., pp.1010-1015, 1997 https://doi.org/10.1109/ITSC.1997.660612
  2. B. Ulmer, 'Vita-An Autonomous Road Vehicle (ARY) for collision avoidance in traffic,' Proc. IEEE Intelligent Vehicles Symp., pp. 36-41,1992 https://doi.org/10.1109/IVS.1992.252230
  3. D. Bishop, 'Vehicle-Highway Automation Activities in the United States,' International AHS Workshop. US Dept. of Transportation, 1997
  4. C. Thorpe and T. Kanade, 'Vision and navigation for the carnegie mellon navlab,' Proc. DARPA Image Understanding Workshop,1985
  5. C. Thorpe, M. Hebert, T. Kanade, and S. Shafer, 'Vision and navigation for the carnegie-mellon navlab,' IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 10, no. 3, pp. 362-373, 1988 https://doi.org/10.1109/34.3900
  6. C. Thorpe, J. D. Carlson, D. Duggins, J. Gowdy, R MacLachlan, C. Mertz, A. Suppe, and C. Wan, 'Safe robot driving in cluttered environments,' Proc. 11th Int'l Symp. Robotics Research, 2003
  7. M. Walton, 'Robots filil to complete grand challenge,' CNN News, Mar. 2004
  8. P. Pascal, 'Stereovision based vehicle classification using support vector machines,' MIT master diploma paper, 2004
  9. Z. Sun, G Bebis, and R Miller, 'On-road vehicle detection using Gabor Filters and support vector machines,' IEEE Proceding DSP 2002, vol. 2, pp. 1019-1022 https://doi.org/10.1109/ICDSP.2002.1028263
  10. N. Paragios and R Deriche, 'Geodesic active contours and level sets for the detection and tracking of moving objects' IEEE Trans-PAMI, vol. 22, pp. 266-280, 2000 https://doi.org/10.1109/TPAMI.2000.845385
  11. S. Beucher and M. Bilodeau, 'Road segmentation and obstacle detection by a fust watershed transformation,' Proc. Int. Veh. Symp, pp. 296-301, 1994 https://doi.org/10.1109/IVS.1994.639531
  12. A. Guacgetti, M. Carnpani, and V. Torre, 'The use of optical flow for road navigation' IEEE Trans. Robitics and Automation, vol. 14, no. 1, pp 34-48,1998 https://doi.org/10.1109/70.660838
  13. Z. Zomotor and U. Franke, 'Sensor fusion for improved vision based lane recognition and objecttracking with range-finders,' IEEE Conf. on Int. Trans. Sys. pp. 595-600, Nov. 1997 https://doi.org/10.1109/ITSC.1997.660541
  14. R O. Duda, P. E . Hart, and D. G Stork, Pattern classification 2nd Edition, Wiley & Sons, Inc., Canada, 2001
  15. 'SVM_light,' http://www.cs.comell.edu/PeopIe/tj/svrn_light/
  16. D. A. Forsyth and J. Ponce Computer Vision: a Modem Approach, Prentice Hall, 2002

Cited by

  1. Design of LDWS Based on Performance-Based Approach Considering Driver Behaviors vol.21, pp.11, 2015, https://doi.org/10.5302/J.ICROS.2015.15.0122