Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

Real-time pedestrian classification exploiting 2D and 3D information

Real-time pedestrian classification exploiting 2D and 3D information

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

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

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.

A new approach for standing and walking pedestrian detection using pattern matching and exploiting both 2D image information and 3D dense stereo information is proposed. Because 3D information accuracy does not allow the direct classification of the 3D shape, a combined 3D–2D method is proposed. The 3D data are used in an innovative way for pedestrian hypotheses generation, scale and depth estimation and 2D models selection. Also the 3D hypotheses allow the corresponding 2D image region of interest selection and the 2D hypothesis generation. The 2D hypothesis consists of the object's external edges obtained by an edge extraction and a depth coherency-based filtering out process. The scaled models are matched against the selected hypothesis using an elastic high-speed matching based on the Chamfer distance. The method has been tested on synthetic and real-world scenarios.

References

    1. 1)
      • Kelly, P., Cooke, E., O'Connor, N.E., Smeaton, A.F.: `Pedestrian detection using stereo and biometric information', Int. Conf. Image Analysis and Recognition ICIAR (2), 2006, p. 802–813.
    2. 2)
      • S. Nedevschi , C. Vancea , T. Marita , T. Graf . On-line calibration method for stereovision systems used in far range detection vehicle applications. IEEE Trans. Intell. Transp. Syst. , 4 , 651 - 660
    3. 3)
      • Gandhi, T., Trivedi, M.: `Pedestrian collision avoidance systems: a survey of computer vision based recent studies', Proc. IEEE Intelligent Transportation Systems Conf., IEEE ITSC, 2006, Toronto, Canada, p. 17–20.
    4. 4)
      • Rucklidge, W.: `Locating objects using the Hausdorff distance', Proc. Int. Conf. Computer Vision, 1995, p. 457–464.
    5. 5)
      • L. Zhao , C. Thorpe . Stereo and neural network-based pedestrian detection. IEEE Trans. Intell. Transp. Syst. , 3 , 148 - 154
    6. 6)
      • M.A. Butt , P. Maragos . Optimum design of chamfer distance transform. IEEE Trans. Image Process. , 10 , 1477 - 1484
    7. 7)
      • Gavrila, D.M.: `Pedestrian detection from a moving vehicle', Proc. European Conf. Computer Vision, 2000, Dublin, p. 37–49.
    8. 8)
      • Broggi, A., Fascioli, A., Grisleri, P., Graf, T., Meinecke, M.: `Model-based validation approaches and matching techniques for automotive vision based pedestrian detection', Proc. 2005 IEEE Computer Society Conf. Computer Vision and Pattern Recognition (CVPR'05) – Workshops, 2005, San Diego, 3, p. 1.
    9. 9)
      • Bertozzi, M., Binelli, E., Broggi, A., Rose, M.D.: `Stereo vision-based approaches for pedestrian detection', Proc. 2005 IEEE Computer Society Conf. Computer Vision and Pattern Recognition (CVPR'05) – Workshops, 2005, 3, p. 16.
    10. 10)
      • D.P. Huttenlocher , G.A. Klanderman , W.J. Rucklidge . Comparing images using the Hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell. , 9 , 850 - 863
    11. 11)
      • Nedevschi, S., Danescu, R., Marita, T.: `A sensor for urban driving assistance systems based on dense stereovision', Proc. IEEE Intelligent Vehicles Symp. (IV 2007), 2007, Istanbul, Turkey.
    12. 12)
      • Gavrila, D., Giebel, J., Munder, S.: `Vision-based pedestrian detection: the PROTECTOR system', Proc. IEEE Intelligent Vehicles Symp, June 2004, p. 13–18.
    13. 13)
      • Gavrila, D.M., Philomin, V.: `Real-time object detection for smart vehicles', Proc. IEEE Int. Conf. Computer Vision, 1999, Kerkyra, Greece, p. 87–93.
    14. 14)
      • Huber, D., Kapuria, A., Donamukkala, R., Hebert, M.: `Parts-based 3D object classification', Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR 04), June 2004, p. 82–89.
    15. 15)
      • Osada, R., Funkhouser, T., Chazelle, B., Dobkin, D.: `Matching 3D models with shape distributions', Proc. Int. Conf. Shape Modeling and Applications, 2001, p. 154–166.
    16. 16)
      • D.M. Gavrila . Sensor-based pedestrain protection. IEEE Intell. Syst. , 5 , 7 - 81
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its_20070038
Loading

Related content

content/journals/10.1049/iet-its_20070038
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address