Multi-class obstacle detection and classification using stereovision and improved active contour models

Multi-class obstacle detection and classification using stereovision and improved active contour models

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

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(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
Your details
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.

Existing in-vehicle sensing systems are concentrated on obstacle detection for pedestrian or vehicle. Limited work has been conducted on multi-class obstacle classification. This study addresses on this issue and aims to develop an approach for simultaneous detection and classification of multi-class obstacles. Stereovision is first used to segment obstacles from traffic background, then an improved active contour model is adopted to extract complete contour curve of the detected obstacles. Based on the contour extracted, geometrical features including aspect ratio, area ratio and height are integrated for classifying object types including vehicle, pedestrian and other obstacles. The approach was tested on substantial urban traffic images and the corresponding results prove the effectiveness of the proposed approach.


    1. 1)
      • 1. Liu, H., Sun, F., He, K.: ‘Symmetry-aided particle filter for vehicle tracking’. IEEE Int. Conf. Robotics Automation, April, 2007, pp. 46334638.
    2. 2)
      • 2. She, K., Bebis, G., Gu, H., et al: ‘Vehicle tracking using on-line fusion of color and shape features’. Proc. 7th Int. IEEE Conf. Intelligent Transportation System, 2004, pp. 731736.
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
      • 7. Lipton, A.: ‘Local application of optic flow to analyze rigid versus non-rigid motion’. Proc. of Int. Conf. on Computer Vision Workshop on Frame-Rate Vision, Corfu, Greece, 1999.
    8. 8)
    9. 9)
    10. 10)
    11. 11)
    12. 12)
    13. 13)
    14. 14)
      • 14. Liu, X., Sun, Z., He, H.: ‘On-road vehicle detection fusing radar and vision’. Proc. of 2011 IEEE Int. Conf. on Vehicular Electronics and Safety (ICVES 2011), 2011, pp. 150154.
    15. 15)
      • 15. Cheng, H., Zheng, N.N., Qin, J.J.: ‘Pedestrian detection using sparse gabor filter and support vector machine’. Proc. of IEEE Intelligent Vehicles Symp., Las Vegas, 2005, pp. 583587.
    16. 16)
    17. 17)
    18. 18)
    19. 19)
    20. 20)
      • 20. Giosan, I., Costea, A.D., Nedevschi, S.: ‘Urban traffic dense-stereo obstacle classification using boosting over visual codebook features’. 2013 IEEE Int. Conf. on Intelligent Computer Communication and Processing (ICCP), September 2013, vol., no., pp. 111116.
    21. 21)
      • 21. Toth, D., Aach, T.: ‘Detection and recognition of moving objects using statistical motion detection and Fourier descriptors’. Proc. of 12th Int. Conf. on Image Analysis and Processing, 2003, pp. 430435.
    22. 22)
      • 22. Lun, Z., Li, S.Z., Xiaotong, Y., et al: ‘Real-time object classification in video surveillance based on appearance learning’. IEEE Conf. on in Computer Vision and Pattern Recognition(CVPR '07), 2007, pp. 18.
    23. 23)
    24. 24)
      • 24. Huang, Y., Ken, Y.: ‘Binocular image sequence analysis: integration of stereo disparity and optic flow for improved obstacle detection and tracking’, EURASIP J. Adv. Signal Process., 2008, 2008, Article ID 843232, p. 10.
    25. 25)
    26. 26)
      • 26. Chen, B., Lai, J.: ‘Active contour models on image segmentation: A survey’, J. Image Graph., 2007, 1, (12), pp. 1120.
    27. 27)
      • 27. Leroy, B., Herlin, I., Cohen, L.D.: ‘Multi-resolution algorithms for active contour models’. 12th Int. Conf. Analysis and Optimization of Systems, 1996, pp. 5865.
    28. 28)
    29. 29)
    30. 30)
    31. 31)
      • 31. ‘KITTI Vision Benchmark Suite’, available at, accessed 18 June 2015.

Related content

This is a required field
Please enter a valid email address