Paper Title
Real-Time Anomaly Detection Surveillance System
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Registration ID: IJNRD_199627
Published ID: IJNRD2306358
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Keywords
Anomaly Detection , CNN
Abstract
Abstract— Today's age put a significant amount of emphasis on monitoring systems. It has received a lot of research recently due to its connection to picture understanding and video analysis. More effective tools that can extract high information content are introduced into the system when artificial intelligence, deep learning, and machine learning are integrated to address the issues with standard style. Most anomaly detection tools focus on indoor surveillance and activity monitoring, which help identify abnormal behavior by watching the live stream footage. The most important of them all is the detection of human nature. It is the most strange behavior, which makes it difficult to assess whether it is normal or suspicious. This study demonstrates how in-depth learning can be used to spot odd behaviour on college or high school campuses. Employing consecutive camera frames obtained from a video, the monitoring is done. We will use our model to find the abnormal behaviour in the extracted camera frames. As soon as an anomaly is found, the stream is saved, alerting the relevant people. Therefore, we only need to save the portion of the video where the anomaly is occurring, rather than recording the entire feed. The system is composed of two parts. In the first section, features will be computed from the live video stream, and the classifier will use those features to forecast the anomaly in the second section. The proposed system can recognize the anomalies with a loss of 4.795. Keywords— LSTM, CNN, Autoencoders, Reconstruction error, Regularity Score
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How To Cite (APA)
Chhabil Nagar, Hemant, Nakul Kaushik, Nayan Tuteja, & Rachana Sharma (June-2023). Real-Time Anomaly Detection Surveillance System. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(6), d566-d572. https://ijnrd.org/papers/IJNRD2306358.pdf
Issue
Volume 8 Issue 6, June-2023
Pages : d566-d572
Other Publication Details
Paper Reg. ID: IJNRD_199627
Published Paper Id: IJNRD2306358
Research Area: Engineering
Author Type: Indian Author
Country: Greater Noida West, Uttar Pradesh, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2306358.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2306358
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