Vehicle Recognition using NMF in Urban Scene 


Vol. 37,  No. 7, pp. 554-564, Jul.  2012


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  Abstract

The vehicle recognition consists of two steps; the vehicle region detection step and the vehicle identification step based on the feature extracted from the detected region. Features using linear transformations have the effect of dimension reduction as well as represent statistical characteristics, and show the robustness in translation and rotation of objects. Among the linear transformations, the NMF(Non-negative Matrix Factorization) is one of part-based representation. Therefore, we can extract NMF features with sparsity and improve the vehicle recognition rate by the representation of local features of a car as a basis vector. In this paper, we propose a feature extraction using NMF suitable for the vehicle recognition, and verify the recognition rate with it. Also, we compared the vehicle recognition rate for the occluded area using the SNMF(sparse NMF) which has basis vectors with constraint and LVQ2 neural network. We showed that the feature through the proposed NMF is robust in the urban scene where occlusions are frequently occur.

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  Cite this article

[IEEE Style]

J. M. Ban, B. Lee, H. Kang, "Vehicle Recognition using NMF in Urban Scene," The Journal of Korean Institute of Communications and Information Sciences, vol. 37, no. 7, pp. 554-564, 2012. DOI: .

[ACM Style]

Jae Min Ban, Byeong-rae Lee, and Hyunchul Kang. 2012. Vehicle Recognition using NMF in Urban Scene. The Journal of Korean Institute of Communications and Information Sciences, 37, 7, (2012), 554-564. DOI: .

[KICS Style]

Jae Min Ban, Byeong-rae Lee, Hyunchul Kang, "Vehicle Recognition using NMF in Urban Scene," The Journal of Korean Institute of Communications and Information Sciences, vol. 37, no. 7, pp. 554-564, 7. 2012.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880