Abstract
The vehicle recognition consists of two steps; the vehicle region detection step and vehicle identification step based on the feature extracted from the detected region. Among the linear transformations, the non-negative matrix factorization (NMF) and non-negative tensor factorization (NTF) can be used in part-based representation. So we can utilize the local features of a car as a basis vector. In this paper, we propose a feature extraction using NMF and NTF suitable for the vehicle recognition, and verify the recognition rate. We show that the proposed feature is robust in the urban scene where occlusions are frequently occur.
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Hwang, M., Kang, H. (2013). A Vehicle Recognition Using Part-Based Representations. In: Jung, HK., Kim, J., Sahama, T., Yang, CH. (eds) Future Information Communication Technology and Applications. Lecture Notes in Electrical Engineering, vol 235. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6516-0_34
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DOI: https://doi.org/10.1007/978-94-007-6516-0_34
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