Novel Perceptive Approach for Automation on Ideal Self-Regulating Video Surveillance Model

Authors

  • Jubber Nadaf Department of Computer Engineering, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India 411043
  • Amol K. Kadam Department of Computer Science and Engineering,Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India 411043
  • Gauri Rao Department of Computer Engineering, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India 411043
  • Yogini Kulkarni Department of Information Technology, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India 411043
  • T. B. Patil Department of Information Technology, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India 411043
  • Manisha Kasar Department of Computer Science and Engineering,Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India 411043

Keywords:

Data Analytics, AWS, ML, S3, IoT, Kinesis, Glue, Automation Video Surveillance

Abstract

Video surveillance systems are essential for public safety and security. Traditional surveillance systems, on the other hand, have limited effectiveness and necessitate extensive human interaction and resources. This research presents a fresh perceptive technique for automation on an ideal self-regulating video surveillance model to address these limitations. The suggested model employs cutting-edge computer vision, machine learning, and deep learning techniques to extract pertinent data from video feeds, identify possible threats and irregularities, and adjust surveillance parameters on its own. Our findings show that the self-regulating model can adapt to changing environments and optimize its surveillance parameters to detect potential threats and anomalies accurately and in real time, resulting in several advantages such as improved accuracy, reduced resource requirements, and real-time analytics for rapid response to potential threats. Our report also offers potential research directions to improve the surveillance system's security and scalability, such as investigating the integration of modern technologies like blockchain and edge computing. Overall, the suggested self-regulating video surveillance model represents a substantial improvement in video surveillance and public safety, with the potential to improve public safety and security in a variety of businesses and circumstances.

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Published

24.03.2024

How to Cite

Nadaf, J. ., Kadam, A. K. ., Rao, G. ., Kulkarni, Y. ., Patil, T. B. ., & Kasar, M. . (2024). Novel Perceptive Approach for Automation on Ideal Self-Regulating Video Surveillance Model. International Journal of Intelligent Systems and Applications in Engineering, 12(19s), 10–17. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5040

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Research Article