Efficient Learning and Classification for Vehicle Type using Moving Cast Shadow Elimination in Vehicle Surveillance Video


KIPS Transactions on Computer and Communication Systems, Vol. 15, No. 1, pp. 1-8, Feb. 2008
10.3745/KIPSTB.2008.15.1.1,   PDF Download:

Abstract

Generally, moving objects in surveillance video are extracted by background subtraction or frame difference method. However, moving cast shadows on object distort extracted figures which cause serious detection problems. Especially, analyzing vehicle information in video frames from a fixed surveillance camera on road, we obtain inaccurate results by shadow which vehicle causes. So, Shadow Elimination is essential to extract right objects from frames in surveillance video. And we use shadow removal algorithm for vehicle classification. In our paper, as we suppress moving cast shadow in object, we efficiently discriminate vehicle types. After we fit new object of shadow-removed object as three dimension object, we use extracted attributes for supervised learning to classify vehicle types. In experiment, we use 3 learning methods {IBL, C4.5, NN(Neural Network)} so that we evaluate the result of vehicle classification by shadow elimination.


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Cite this article
[IEEE Style]
W. S. Shin and C. H. Lee, "Efficient Learning and Classification for Vehicle Type using Moving Cast Shadow Elimination in Vehicle Surveillance Video," KIPS Journal B (2001 ~ 2012) , vol. 15, no. 1, pp. 1-8, 2008. DOI: 10.3745/KIPSTB.2008.15.1.1.

[ACM Style]
Wook Sun Shin and Chang Hoon Lee. 2008. Efficient Learning and Classification for Vehicle Type using Moving Cast Shadow Elimination in Vehicle Surveillance Video. KIPS Journal B (2001 ~ 2012) , 15, 1, (2008), 1-8. DOI: 10.3745/KIPSTB.2008.15.1.1.