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Proceedings Paper

Vehicle detection and classification using robust shadow feature
Author(s): Chae Whan Lim; Jong-Sun Park; Chang-Sup Lee; Nam Chul Kim
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Paper Abstract

We propose an efficient vehicle detection and classification algorithm using shadow robust feature for an electronic toll collection. The local correlation coefficient between wavelet transformed input and reference images is used as such a feature, which takes advantage of textural similarity. The usefulness of the proposed feature is analyzed qualitatively by comparing the feature with the local variance of a difference image, and is verified by measuring the improvements in the separability of vehicle from shadowy or shadowless road for a real test image. Experimental results from field tests show that the proposed vehicle detection and classification algorithm performs well even under abrupt intensity change due to the characteristics of sensor and occurrence of shadow.

Paper Details

Date Published: 28 December 1998
PDF: 10 pages
Proc. SPIE 3653, Visual Communications and Image Processing '99, (28 December 1998); doi: 10.1117/12.334632
Show Author Affiliations
Chae Whan Lim, Kyungpook National Univ. (South Korea)
Jong-Sun Park, LG Electronics Inc. (South Korea)
Chang-Sup Lee, Kyungpook National Univ. (South Korea)
Nam Chul Kim, Kyungpook National Univ. (South Korea)


Published in SPIE Proceedings Vol. 3653:
Visual Communications and Image Processing '99
Kiyoharu Aizawa; Robert L. Stevenson; Ya-Qin Zhang, Editor(s)

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