Over-Height Vehicle Detection in Low Headroom Roads Using Digital Video Processing
In this paper we present a new method for over-height
vehicle detection in low headroom streets and highways using digital
video possessing. The accuracy and the lower price comparing to
present detectors like laser radars and the capability of providing
extra information like speed and height measurement make this
method more reliable and efficient. In this algorithm the features are
selected and tracked using KLT algorithm. A blob extraction
algorithm is also applied using background estimation and
subtraction. Then the world coordinates of features that are inside the
blobs are estimated using a noble calibration method. As, the heights
of the features are calculated, we apply a threshold to select overheight
features and eliminate others. The over-height features are
segmented using some association criteria and grouped using an
undirected graph. Then they are tracked through sequential frames.
The obtained groups refer to over-height vehicles in a scene.
[1] CSE Intelligent Transport Systems, online:
http://www.cse-global.com/solutions/its_product.asp
[2] L. A. Klein, M. K. Mills and D. R. Gibson, Traffic Detector Handbook.
vol. 1, Federal Highway Administration, 2006.
[3] S. Gupte, O. Masoud, R. F. K. Martin, and N. P. Papanikolopoulos,
"Detection and classification of vehicles", IEEE Trans. on Intelligent
Transportation Systems, vol. 3, pp. 37-47, Mar. 2002.
[4] D. Magee, "Tracking multiple vehicles using foreground, background
and motion models", Image and Vision Computing, vol 22(2), pp. 143-
155, 2004.
[5] M. Haag and H. Nagel, "Combination of edge element and optical flow
estimate for 3D-model-based vehicle tracking in traffic image
sequences", International Journal of Computer Vision, pp. 295-319,
1999.
[6] N. Kanhere, S. Pundlik and S. Birchfield, "Vehicle segmentation and
tracking from a low-angle off-axis camera", in IEEE Conference on
ComputerVision and Pattern Recognition, pp. 1152-1157, 2005.
[7] E. Trucco and A. Verri, Introductory Techniques for 3-D Computer
Vision. Prentice Hall, 1998, pp. 39.
[8] X. Fu, Z. Wang, D. Liang and J. Jiang, " The Extraction of Moving
Object in Real-Time Web-Based Video Sequence", in The 8th
International Conference on Digital Object Identifier, Vol. 1, pp. 187-
190, 2004.
[9] C. Tomasi and T. Kanade, "Detection and tracking of point features,"
Carnegie Mellon University , Technical Report CMU-CS-91-132, 1991.
[1] CSE Intelligent Transport Systems, online:
http://www.cse-global.com/solutions/its_product.asp
[2] L. A. Klein, M. K. Mills and D. R. Gibson, Traffic Detector Handbook.
vol. 1, Federal Highway Administration, 2006.
[3] S. Gupte, O. Masoud, R. F. K. Martin, and N. P. Papanikolopoulos,
"Detection and classification of vehicles", IEEE Trans. on Intelligent
Transportation Systems, vol. 3, pp. 37-47, Mar. 2002.
[4] D. Magee, "Tracking multiple vehicles using foreground, background
and motion models", Image and Vision Computing, vol 22(2), pp. 143-
155, 2004.
[5] M. Haag and H. Nagel, "Combination of edge element and optical flow
estimate for 3D-model-based vehicle tracking in traffic image
sequences", International Journal of Computer Vision, pp. 295-319,
1999.
[6] N. Kanhere, S. Pundlik and S. Birchfield, "Vehicle segmentation and
tracking from a low-angle off-axis camera", in IEEE Conference on
ComputerVision and Pattern Recognition, pp. 1152-1157, 2005.
[7] E. Trucco and A. Verri, Introductory Techniques for 3-D Computer
Vision. Prentice Hall, 1998, pp. 39.
[8] X. Fu, Z. Wang, D. Liang and J. Jiang, " The Extraction of Moving
Object in Real-Time Web-Based Video Sequence", in The 8th
International Conference on Digital Object Identifier, Vol. 1, pp. 187-
190, 2004.
[9] C. Tomasi and T. Kanade, "Detection and tracking of point features,"
Carnegie Mellon University , Technical Report CMU-CS-91-132, 1991.
@article{"International Journal of Information, Control and Computer Sciences:50142", author = "Vahid Khorramshahi and Alireza Behrad and Neeraj K. Kanhere", title = "Over-Height Vehicle Detection in Low Headroom Roads Using Digital Video Processing", abstract = "In this paper we present a new method for over-height
vehicle detection in low headroom streets and highways using digital
video possessing. The accuracy and the lower price comparing to
present detectors like laser radars and the capability of providing
extra information like speed and height measurement make this
method more reliable and efficient. In this algorithm the features are
selected and tracked using KLT algorithm. A blob extraction
algorithm is also applied using background estimation and
subtraction. Then the world coordinates of features that are inside the
blobs are estimated using a noble calibration method. As, the heights
of the features are calculated, we apply a threshold to select overheight
features and eliminate others. The over-height features are
segmented using some association criteria and grouped using an
undirected graph. Then they are tracked through sequential frames.
The obtained groups refer to over-height vehicles in a scene.", keywords = "Feature extraction, over-height vehicle detection,
traffic monitoring, vehicle tracking.", volume = "2", number = "3", pages = "662-5", }