Volume: 2 Issue: 1
Year: 2025, Page: 90-96, Doi: https://doi.org/10.70968/ijeaca.v2i1.D1004
Received: Jan. 25, 2025 Accepted: May 10, 2025 Published: June 28, 2025
In recent decades, surveillance cameras have been widely deployed in various locations for security and monitoring. The video data analysis captured by these cameras plays a crucial role in event prediction, real-time tracking, and goal-driven applications such as anomaly and intrusion detection. With advancements in Artificial Intelligence (AI), deep learning-based approaches, particularly Convolutional Neural Networks (CNNs), have significantly improved anomaly detection accuracy. This study proposes a novel deep-learning framework for detecting anomalies in video surveillance, leveraging CNNs for spatial feature extraction. In recent decades, surveillance cameras have been widely deployed in various locations for security and monitoring. The video data analysis captured by these cameras plays a crucial role in event prediction, real-time tracking, and goal-driven applications such as anomaly and intrusion detection. With advancements in Artificial Intelligence (AI), deep learning-based approaches, particularly Convolutional Neural Networks (CNNs), have significantly improved anomaly detection accuracy. This study proposes a novel deep-learning framework for detecting anomalies in video surveillance, leveraging CNNs for spatial feature extraction. The approach is inspired by previous studies, such as "Anomaly Detection in Surveillance Videos Using Deep Learning" 1, which demonstrated high efficiency in recognizing abnormal patterns. The UCSD dataset has been used to assess the suggested approach, showing improved accuracy in anomaly detection compared to existing methods. The findings highlight the potential of deep learning in enhancing automated surveillance systems, contributing to intelligent security monitoring and public safety. 1
Keywords: Anomaly Detection in Videos Using Deep Learning
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© 2025 Patil. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Patil A. (2025). Anomaly Detection in Videos Using Deep Learning. International Journal of Electronics and Computer Applications. 2(1): 90-96. https://doi.org/10.70968/ijeaca.v2i1.D1004