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Person Re-identification by Local Feature Based on Super Pixel

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7732))

Abstract

In many multi-camera surveillance systems, there is a need to identify whether a captured person have emerged before over the network of cameras. This is the person re-identification problem. In this paper, we propose a novel re-identification method based on super pixel feature. Firstly, local C-SIFT features based on super pixel are extracted as visual words, and appearance details are used to describe detecting objects. Secondly, a TF-IDF vocabulary index tree is built to speed up person search. Finally, an image-retrieval way is adopted to implement person re-identification. Experimental results on ETHZ dataset show that our method is better than the approach proposed by Schwartz et.al and two machine learning methods based on SVM and PCA.

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Liu, C., Zhao, Z. (2013). Person Re-identification by Local Feature Based on Super Pixel. In: Li, S., et al. Advances in Multimedia Modeling. MMM 2013. Lecture Notes in Computer Science, vol 7732. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35725-1_18

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  • DOI: https://doi.org/10.1007/978-3-642-35725-1_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35724-4

  • Online ISBN: 978-3-642-35725-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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