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Camera Feature Ranking for Person Re-Identification Using Deep Learning

In: New Trends in Computational Vision and Bio-inspired Computing

Author

Listed:
  • S. Akshaya

    (SRM Institute of Science and Technology, Department of Big Data Analytics)

  • S. Lavanya

    (SRM Institute of Science and Technology)

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.

Suggested Citation

  • S. Akshaya & S. Lavanya, 2020. "Camera Feature Ranking for Person Re-Identification Using Deep Learning," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 1275-1281, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41862-5_129
    DOI: 10.1007/978-3-030-41862-5_129
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