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Person reidentification on video surveillance data
Author(s): Andrey Kuznetsov
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Paper Abstract

Person reidentification is a very challenging problem nowadays because of a big amount of video surveillance systems used. The data from such systems is processed to analyze events or emergency situations, find specific people, etc. One of the ways of solving the problem of an area security is the development of person reidentification algorithms. In this paper we propose an algorithm for person reidentification based on RGB histogram features calculation. On the first stage HOG descriptor is selected to detect a person on an image. Then we used k-means++ clustering algorithm to remove background on a person image. Finally, Bayes and SVM classification methods were used for person reidentification. Experimental results showed that the proposed solution can be used for person reidentification with high precision (not less than 82%). To carry out research 3D People Surveillance Dataset was used.

Paper Details

Date Published: 15 March 2019
PDF: 6 pages
Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110410P (15 March 2019); doi: 10.1117/12.2522932
Show Author Affiliations
Andrey Kuznetsov, Samara National Research Univ. (Russian Federation)


Published in SPIE Proceedings Vol. 11041:
Eleventh International Conference on Machine Vision (ICMV 2018)
Antanas Verikas; Dmitry P. Nikolaev; Petia Radeva; Jianhong Zhou, Editor(s)

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