Open Access
Swati Verma
Vikas Jaiswal
Radhey Shyam
- Student Department of Computer Science and Engineering, Shri Ramswaroop Memorial College of Engineering and Management (SRMCEM), Lucknow Uttar Pradesh India
- Professor Department of Computer Science and Engineering, Shri Ramswaroop Memorial College of Engineering and Management SRMCEM, Lucknow Uttar Pradesh India
Abstract
The smart video surveillance system is designed to observe the activities of people nearby for safety concerns. This smart video surveillance can be installed at malls, public areas, banks, businesses, and ATM machines. The field of network surveillance research is rapidly expanding. The cause for this is the global insecurity that is occurring in great extent. As a result, an intelligent monitoring system that gathers data in real time, communicates, analyses, and interprets information about individuals being observed is required. Video surveillance systems are in great demand due to increasing safety concerns. The automated anomaly detection systems are much better since they require almost negligible human intervention and can more accurately detect anomalies. Besides this, the data can be easily stored and retrieved with the help of the video surveillance system. Video surveillance systems may be used for a variety of purposes, including traffic monitoring and anomaly detection. The study describes how three- dimensional deep neural networks are used to learn spatiotemporal elements of video feeds that can be used in our smart surveillance system.
Keywords: Automated anomaly detection, deep neural networks, spatiotemporal, video surveillance system, CNN
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Recent Trends in Programming languages
| Volume | |
| Received | May 19, 2022 |
| Accepted | June 4, 2022 |
| Published | January 4, 2023 |