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Deep Learning in Person Re-identification for Cyber-Physical Surveillance Systems

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Abstract

The Cyber-physical Systems (CPS) are a combination of integrated physical processes, networking and computation to be minored and controlled y embedded subsystems via networked systems with feedback loops to change their behaviour when needed. Whilst the increased use of CPS brings more threats to the public, and thus security problems in this area have become a global issue to make it necessary to develop new approaches for securing CPS. The CPS utilise three-level architecture based on the respective functions of each layer: the perception layer, the transmission layer, and the application layer. Security in specific, CPS applications is currently the most important security objective of CPS because it offers the importance of CPS in its improving functionality

This chapter focuses on the application aspect which is more related to people’s daily lives, and will present a real-time system including distributed multi-camera system that integrates computing and communicating capabilities with monitoring on people in the physical world, namely person re-identification in the cyber-physical surveillance systems. The increasing sophistication and diversity of threats to public security have been causing a critical demand for the development and deployment of reliable, secure, and time-efficient visual intelligent surveillance systems in smart cities. For example, visual surveillance for indoor environments, like metro stations, plays an important role both in the assurance of safety conditions for the public and in the management of the transport network. Recent progress in computer vision techniques and related visual analytics offers new prospects for an intelligent surveillance system. A major recent development is the massive success resulting from using deep learning techniques to enable a significant boosting to visual analysis performance and initiate new research directions to understand visual content. For example, convolutional neural networks have demonstrated superiority on modelling high-level visual concepts. It is expected that the development of deep learning and its related visual analytic methodologies would further influence the field of intelligent surveillance systems. In view of the high demand for a prevalent surveillance system by the metropolis communities, this chapter will introduce recent research based on deep neural networks and pipelines to the practitioners and human investigators undertaking forensic and security analysis of large volumes of open-world CCTV video data sourced from a large distributed multi-camera network covering complex urban environments with transport links. This chapter will address the challenges of using deep learning and related techniques to understand and promote the use of ubiquitous intelligent surveillance systems.

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Authors and Affiliations

  1. The University of Queensland, Brisbane, QLD, Australia

    Lin Wu & Brian C. Lovell

  2. Hefei University of Technology, Hefei, China

    Yang Wang

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  1. Lin Wu
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  2. Brian C. Lovell
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  3. Yang Wang
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Correspondence to Lin Wu .

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Editors and Affiliations

  1. Charles Darwin University, Casuarina, NT, Australia

    Mamoun Alazab

  2. Singtel Optus, Sydney, NSW, Australia

    MingJian Tang

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Wu, L., Lovell, B.C., Wang, Y. (2019). Deep Learning in Person Re-identification for Cyber-Physical Surveillance Systems. In: Alazab, M., Tang, M. (eds) Deep Learning Applications for Cyber Security. Advanced Sciences and Technologies for Security Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-13057-2_3

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  • DOI: https://doi.org/10.1007/978-3-030-13057-2_3

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