Improving backing-up manoeuvre safety with vision-based movement detection

Access Full Text

Improving backing-up manoeuvre safety with vision-based movement detection

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Add to cart
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)
Add to cart

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Intelligent Transport Systems — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

© The Institution of Engineering and Technology
Published

Every year, backing-up crashes are responsible for hundreds of casualties in the world. In order to reduce this number, many automotive suppliers now provide ways for drivers to monitor the rear of their vehicle using sonar - or radar-based systems or wide angle cameras. Yet these systems still do not offer the performance required to consequently reduce the number of fatalities. To overcome the limitations of these systems, we have developed a real-time static obstacle detection system using a wide angle camera. We now propose to add moving obstacle detection and localisation to our camera-based system. Such system is the perfect candidate for next generation of automatic anti-collision systems. Our approach is based on camera movement compensation using inverse perspective mapping and probabilistic reasoning for movement detection. Using shape filtering, our approach can even limit detection to specific types of moving objects (pedestrians, cars etc.). We present the algorithms developed and the evaluation protocol used. We also show results of the system in typical urban situations, some in hard conditions (worst cases), and conclude on which future developments will bring performance improvements.

Inspec keywords: collision avoidance; traffic engineering computing; road vehicles; motion compensation; cameras; object detection

Other keywords: real-time static obstacle detection system; camera-based system; urban situations; automatic anti-collision systems; shape filtering; inverse perspective mapping; camera movement compensation; moving obstacle detection; wide angle camera; backing-up manoeuvre safety; vision-based movement detection; localisation; evaluation protocol; probabilistic reasoning

Subjects: Optical, image and video signal processing; Computer vision and image processing techniques; Spatial variables control; Traffic engineering computing; Image sensors

References

    1. 1)
    2. 2)
      • Fablet, R., Black, M.J.: `Automatic detection and tracking of human motion with a view-based representation', Proc. of European Conf. on Computer Vision, 2002, p. 476–491.
    3. 3)
      • Bertozzi, M., Broggi, A., Fascioli, A., Lombardi, P.: `Vision-based pedestrian detection: will ants help?', Proc. of IEEE Intelligent Vehicles Symp, June 2002, Versailles.
    4. 4)
      • A. Broggi , A. Fascioli , M. Carletti , T. Meinecke . A multi-resolution approach for infrared vision-based pedestrian detection. IEEE Intell. Vehicle Symp. , 7 - 12
    5. 5)
    6. 6)
      • Masoud, O., Papanikolopoulos, N.P.: `A robust real-time multi-level model-based pedestrian tracking system', Proc. of the ITS America Seventh Annual Meeting, June 1997, Washington, DC.
    7. 7)
      • Fintzel, K., Bendahan, R., Vestri, C., Bougnoux, S., Kakinami, T.: `3D parking assistant system', 11thWorld Congress on Intelligent Transport Systems, 2004, Nagoya, Japan.
    8. 8)
      • Giebel, J., Gavrila, D.M.: `A Bayesian framework for Multi-cue 3D object tracking', Proc. of European Conf. on Computer Vision, 2004, Springer, p. 241–252.
    9. 9)
      • Abramson, Y., Steux, B.: `Hardware-friendly pedestrian detection and impact prediction', Proc. of IEEE Intelligent Vehicles Symp, June 2004.
    10. 10)
      • Sidenbladh, H.: `Detecting human motion with support vector machines', Proc. of IAPR Int. Conf. on Pattern Recognition, 2004, 2, Cambridge, UK, p. 188–191.
    11. 11)
      • Fuerstenberg, K.Ch., Dietmayer, K.C.J., Eisenlauer, S., Willhoeft, V.: `Multilayer laserscanner for robust object tracking and classification in urban traffic scenes', Proc. of ITS 2002, 9th World Congress on Intell. Transport Systems, October 2002, Chicago, ITS 2002, Paper 2054..
    12. 12)
      • AISIN SEIKI Parking Assist System, http://www.aisin.com/products/auto/info.html, accessed November 2006.
    13. 13)
    14. 14)
      • L. Zhao , C. Thorpe . Stereo- and neural network-based pedestrian detection. IEEE Trans. Intell. Trans. Syst. , 3 , 148 - 154
    15. 15)
      • Takayama, M., Tanaka, Y., Kakinami, T., Iwata, Y.: `Driving assistance system using rear-view camera', 8thWorld Congress on Intelligent Transport Systems, October 2001, Sydney, Australia.
    16. 16)
      • Papageorgiou, C., Poggio, T.: `Trainable pedestrian detection', Proc. of Int. Conf. on Image Processing (ICIP'99), October 1999, Kobe, Japan.
    17. 17)
      • U. Franke , D.M. Gavrila , S. Gorzig , F. Lindner , F. Paetzold , C. Wohler . Autonomous driving goes downtown. IEEE Intell. Syst. , 6 , 40 - 48
    18. 18)
      • Lee, L., Dalley, G., Tieu, K.: `Learning pedestrian models for silhouette refinement', Proc. of Int. Conf. on Computer Vision and Pattern Recognition, 2003.
    19. 19)
      • Broggi, A., Bertozzi, M., Fascoli, A., Sechi, M.: `Shape-based pedestrian detection', Proc. of IEEE Intelligent Vehicle Symp, 2000, Dearborn, U.S.A, p. 215–220.
    20. 20)
      • , : `Vehicle backover avoidance technology study', Report to Congress, November 2006.
    21. 21)
      • Shashua, A., Gdalyahu, Y., Hayon, G.: `Pedestrian detection for driving assistance systems: single-frame classification and system level performance', Proc. of the IEEE Intelligent Vehicles Symp, (IV2004), June 2004, Parma, Italy.
    22. 22)
      • Lipton, A., Fujiyoshi, H., Patil, R.: `Moving target classification and tracking from real-time video', Proc. of the 1998 DARPA Image Understanding Workshop (IUW'98), November 1998.
    23. 23)
      • Gavrila, D., Philomin, V.: `Real-time object detection for smart vehicles', Proc. of Int. Conf. on Computer Vision, 1999, p. 87–93.
    24. 24)
      • Lefaix, G., Marchand, E., Bouthemy, P.: `Motion-based obstacle detection and tracking for car driving assistance', Proc. of IAPR Int. Conf. on Pattern Recognition (ICPR'2002), August 2002, Québec, Canada, 4, p. 74–77.
    25. 25)
      • http://www.toyota.co.jp/en/tech/its/program/function/parking.html, accessed November 2006.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its_20060060
Loading

Related content

content/journals/10.1049/iet-its_20060060
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading