Abstract
This paper presents a vehicle recognition approach for a real transportation surveillance system using sparse coding. Comparison between sparse coding and conventional histogram of orientation gradient (HOG) has been studied. The results showed that the sparse coding learned feature is better than HOG feature in such vehicle recognition application. Experiments indicated that overlapping spatial pooling over the learned sparse codes can improve accuracy in a great deal.
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Zeng, S., Niu, X., Dou, Y. (2014). Vehicle Recognition for Surveillance Video Using Sparse Coding. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45643-9_24
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DOI: https://doi.org/10.1007/978-3-662-45643-9_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45642-2
Online ISBN: 978-3-662-45643-9
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