Camera Feature Ranking for Person Re-Identification Using Deep Learning

S. Akshaya () and S. Lavanya ()
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S. Akshaya: SRM Institute of Science and Technology, Department of Big Data Analytics
S. Lavanya: SRM Institute of Science and Technology

A chapter in New Trends in Computational Vision and Bio-inspired Computing, 2020, pp 1275-1281 from Springer

Abstract: Abstract Recent Days, automated video surveillance is a major part of security in banks, streets, air ports, railway stations, and crowded areas with multiple cameras. One significant reason to choose automated video surveillance is that, it identifies suspects involved in suspicious activities which will give lead for further investigations. In this work the proposed system will re-identify the suspect face from various surveillance cameras which is been deployed in different locations or positions of street or building, etc. The proposed trained Convolution Neural Network model will extract the facial features. The facial features that are extracted from multiple cameras, are the given to feature ranking algorithm to identifies the frame with the maximum features. As a result, the model will be able to detect the person from multiple cameras which reduces the manual monitoring.

Keywords: Deep learning; Multiple camera; Surveillance; Feature ranking; Convolutional neural networks (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-41862-5_129

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DOI: 10.1007/978-3-030-41862-5_129

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