| Issue |
E3S Web of Conf.
Volume 529, 2024
International Conference on Sustainable Goals in Materials, Energy and Environment (ICSMEE’24)
|
|
|---|---|---|
| Article Number | 04009 | |
| Number of page(s) | 10 | |
| Section | Advanced Interdisciplinary Approaches | |
| DOI | https://doi.org/10.1051/e3sconf/202452904009 | |
| Published online | 29 May 2024 | |
Sustainable Abnormal Events Detection and Tracking in Surveillance System
1 Department of Computer Science and Engineering, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, Telangana, India
2 The Islamic university, Najaf, Telangana, Iraq
* Corresponding author: khandekarapurva@gmail.com
With the proliferation of surveillance cameras, managing and analyzing vast amounts of video data have become challenging. This paper introduces a sustainable automated approach to detect abnormal events in surveillance footage. Leveraging Convolutional Neural Networks (CNNs) and deep learning techniques, our system identifies unusual activities by analyzing video frames. By automating this process, we reduce the burden of manual monitoring and enable timely responses to security threats. This sustainable solution has broad applications in public safety, security, and crime prevention.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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