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3 articles for “Intelligent video surveillance”
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Intelligence Video Surveillance Using Deep Learning
Abstract: Intelligent video surveillance powered by deep learning represents a transformative paradigm in security and surveillance. This new approach uses the power of complex neural networks to revolutionize the analysis and management of video data. Through the seamless incorporation of deep learning technologies like convolutional neural networks (CNN) and recurrent neural networks (RNN) into observation systems, automated identification, tracking, and classification of objects can be accomplished, alleviating storage constraints on the …
Published in International Journal of Software Computing and Testing · Vol. 10, Issue 1, 2024 · pp. 9–20 Read article
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Anomaly Detection Network for Surveillance Video
Abstract: Anomaly Activity is the prediction of a suspicious activity from a picture or video. This project would include using neural networks to detect suspicious human activity from real-time CCTV video. Human Anomaly Activity is a central issue in computer vision that has been researched for over 15 years. It is significant due to the large number of applications that can benefit from Activity detection. Human pose estimation, for example, is …
Published in International Journal of Software Computing and Testing · Vol. 7, Issue 2, 2021 · pp. 13–20 Read article
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A Review of Real-Time Human Detection
Abstract: The real-time human detection and counting system leverages deep learning and computer vision to detect individuals, classify gender, and estimate age from live video feeds or CCTV footage. Implemented using flask as a lightweight web framework, the system manages user authentication, detection control, and real-time result display. A Python-based detection script operates asynchronously, ensuring smooth execution, while results – including counts of males, females, and total individuals – are stored …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 11, Issue 2, 2025 · pp. 29–39 Read article