A comprehensive survey on machine learning techniques to mobilize multi-camera network for smart surveillance
Computer vision; Edge computing; Fog computing; Internet of things; Machine learning; Object detection; Object tracking
Deploying a web of CCTV cameras for surveillance has become an integral part of any smart citys security procedure. This, however, has led to a steady increase in the number of cameras being deployed. These cameras generate a large amount of data, which needs to be further analyzed. Our next step is to achieve a network of cameras spread across a city that does not require any human assistance to detect, recognize and track a person. This paper incorporates various algorithmic techniques used in order to make surveillance systems and their use cases so as to enable less human intervention dependent as much as possible. Even though many of these methods do carry out the task graciously, there are still quite a few obstructions such as computational resources required for model building, training time for the models, and many more issues that hinder the process and hence, constrain the possibility of easy implementation. In this paper, we also intend to shift the paradigm by providing evidence toward the use of technologies like Fog computing and edge computing coupled with the surveillance technology trends, which can help to achieve the goal in a sustainable manner with lesser overheads. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023.
Dharan, Anandu M.; Mukhopadhyay, Debarka
Innovations in Systems and Software Engineering;Volume;21;Issue;1;Article No.;100355;pp.313-332
Springer Science and Business Media Deutschland GmbH
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https://doi.org/10.1007/s11334-023-00533-2 https://www.scopus.com/pages/publications/105001090583?origin=resultslist
Dharan A.M., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Kengeri Campus, Bangalore, India; Mukhopadhyay D., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Kengeri Campus, Bangalore, India