Sustainable Abnormal Events Detection and Tracking in Surveillance System
| Title: | Sustainable Abnormal Events Detection and Tracking in Surveillance System |
|---|---|
| Authors: | Khandekar Apurva, Meneni Vikas, Uddin Haseeb, Nikhil Bhusa, Tejeshwar M., Riad Al-Fatlawy Ramy |
| Source: | E3S Web of Conferences, Vol 529, p 04009 (2024) |
| Publisher Information: | EDP Sciences, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Environmental sciences |
| Subject Terms: | Environmental sciences, GE1-350 |
| More Details: | 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. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English French |
| ISSN: | 2267-1242 |
| Relation: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/59/e3sconf_icsmee2024_04009.pdf; https://doaj.org/toc/2267-1242 |
| DOI: | 10.1051/e3sconf/202452904009 |
| Access URL: | https://doaj.org/article/a281bc98226243a4b2683cde345eb04b |
| Accession Number: | edsdoj.281bc98226243a4b2683cde345eb04b |
| Database: | Directory of Open Access Journals |
| ISSN: | 22671242 |
|---|---|
| DOI: | 10.1051/e3sconf/202452904009 |
| Published in: | E3S Web of Conferences |
| Language: | English French |