Browsing by Author "TOUATI Hayet"

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    Intelligent Video Recording Optimization using Activity Detection for ESTIN Surveillance Systems
    (Tassadit, 2025-01-21) MELIZOU Ouassila; TOUATI Hayet
    Surveillance systems often face the challenge of managing extensive amounts of footage, much of which is irrelevant, leading to inefficient storage and difficulty in event retrieval. This thesis addresses this issue by proposing an optimized video recording solution that focuses on activity detection. The proposed approach utilizes a hybrid method combining motion detection via frame subtraction and object detection using You Look Only Once model . This strategy aims to record only scenes with human activity, thereby reducing unnecessary footage and optimizing storage usage. The developed model demonstrates superior performance, achieving precision metrics of 0.855 for car detection and 0.884 for person detection, highlighting its effectiveness in enhancing the efficiency of surveillance systems. However, some limitations remain, such as false positives and false negatives in bad weather conditions like powerful winds.
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    Review of Techniques for Optimizing Intelligent Video Recording Using Activity Detection in Surveillance Systems
    (Tassadit, 2025-01-25) MELIZOU Ouassila; TOUATI Hayet
    This master thesis presents a comprehensive review of various activity detection methods that can be used in surveillance systems to capture relevant footage. The review encompasses twenty articles, covering a wide range of intelligent video system approaches. The key methodologies examined include background subtraction, optical flow, machine learning techniques such as Support Vector Machines, and deep learning techniques including You Only Look Once, Convolutional Neural Networks, and Faster Region-based Convolutional Neural Networks. Each method is thoroughly explained, with an emphasis on their respective strengths and weaknesses. The analysis provides insights into the current state of the art and identifies potential areas for future research and development in surveillance systems.