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1996 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'96)
Efficient Image Gradient-Based Object Localisation and Recognition
San Francisco, Ca.
June 18-June 20
ISBN: 0-8186-7258-7
| ASCII Text | x | ||
| T. N. Tan, G. D. Sullivan, K. D. Baker, "Efficient Image Gradient-Based Object Localisation and Recognition," 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 397, 1996 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'96), 1996. | |||
| BibTex | x | ||
| @article{ 10.1109/CVPR.1996.517103, author = {T. N. Tan and G. D. Sullivan and K. D. Baker}, title = {Efficient Image Gradient-Based Object Localisation and Recognition}, journal ={2012 IEEE Conference on Computer Vision and Pattern Recognition}, volume = {0}, year = {1996}, issn = {1063-6919}, pages = {397}, doi = {http://doi.ieeecomputersociety.org/10.1109/CVPR.1996.517103}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - 2012 IEEE Conference on Computer Vision and Pattern Recognition TI - Efficient Image Gradient-Based Object Localisation and Recognition SN - 1063-6919 SP EP A1 - T. N. Tan, A1 - G. D. Sullivan, A1 - K. D. Baker, PY - 1996 KW - model-based vision KW - vehicle localization KW - traffic scene analysis object recognition KW - ground-plane constraint VL - 0 JA - 2012 IEEE Conference on Computer Vision and Pattern Recognition ER - | |||
This paper reports novel algorithms for the efficient localization and recognition of vehicles in traffic scenes, which eliminate the need for explicit symbolic feature extraction and matching. The algorithms make use of two a priori sources of knowledge about the scene and the objects: (i) the ground-plane constraint, and (ii) the fact that road vehicles are strongly rectilinear. The algorithms are demonstrated and tested using routine outdoor traffic images. Success with a variety of vehicles demonstrates the efficiency and robustness of context-based computer vision in road traffic scenes. The limitations of the algorithms are also addressed in the paper.
Index Terms:
model-based vision, vehicle localization, traffic scene analysis object recognition, ground-plane constraint
Citation:
T. N. Tan, G. D. Sullivan, K. D. Baker, "Efficient Image Gradient-Based Object Localisation and Recognition," cvpr, pp.397, 1996 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'96), 1996
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