DocumentCode :
2634786
Title :
Efficient image gradient-based object localisation and recognition
Author :
Tan, T.N. ; Sullivan, G.D. ; Baker, K.D.
Author_Institution :
Dept. of Comput. Sci., Reading Univ., UK
fYear :
1996
fDate :
18-20 Jun 1996
Firstpage :
397
Lastpage :
402
Abstract :
This paper reports novel algorithms for the efficient localisation 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 rectilineal: 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
Keywords :
computer vision; object recognition; road traffic; traffic engineering computing; computer vision; feature extraction; image gradient-based; object localisation; recognition of vehicles; road traffic scenes; traffic images; Computer science; Computer vision; Feature extraction; Image recognition; Land vehicles; Layout; Road vehicles; Robustness; Solid modeling; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 1996. Proceedings CVPR '96, 1996 IEEE Computer Society Conference on
Conference_Location :
San Francisco, CA
ISSN :
1063-6919
Print_ISBN :
0-8186-7259-5
Type :
conf
DOI :
10.1109/CVPR.1996.517103
Filename :
517103
Link To Document :
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