Published 2025-12-24
Keywords
- Anomaly detection,
- video surveillance,
- machine learning,
- IoT,
- real-time processing
How to Cite
Copyright (c) 2025 Swami Vivekananda University Press
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Abstract
The integration of advanced technologies into video surveillance systems has transformed security applications in various sectors. A powered video surveillance system equipped with anomaly detection capabilities offers a proactive approach to identifying and responding to irregular events in real-time. This paper explores the architecture, algorithms, and practical applications of a powered video surveillance system for anomaly detection. The system leverages cutting-edge machine learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to analyze video streams and identify deviations from normal patterns. These models are trained using large datasets comprising diverse scenarios to enhance accuracy and reliability. The proposed system also incorporates edge computing for real-time processing and minimizes latency issues associated with cloud-based solutions [1].
Key challenges addressed in this study include handling large-scale data, optimizing computational resources, and reducing false-positive rates. The paper provides a detailed analysis of data preprocessing techniques, feature extraction methods, and model optimization strategies. Additionally, the integration of Internet of Things (IoT) devices with the surveillance system enables seamless communication and enhanced scalability [2]. Real-world case studies are presented to demonstrate the efficacy of the system in detecting anomalies, such as unauthorized access, theft, and violence. The results highlight the system’s capability to adapt to dynamic environments and provide actionable insights for security personnel.
This research contributes to the growing field of intelligent surveillance systems by offering a comprehensive framework for anomaly detection. Future work will focus on improving energy efficiency, integrating advanced sensors, and exploring the ethical implications of video surveillance. Keywords for this study include anomaly detection, video surveillance, machine learning, IoT, and real-time processing.
DOI - https://doi.org/10.65525/SVUP.9788199651517.2026.101-106