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I will introduce our research on improving CCTV-based intrusion detection systems. CCTV plays a crucial role in protecting public spaces, businesses, and residential areas by continuously monitoring activities and detecting potential threats.
Security is a key concern in various sectors. CCTV surveillance is widely used to ensure safety by detecting unauthorized activities in real time. Intrusion detection helps protect both assets and individuals by providing timely alerts and enabling quick responses.
Traditional intrusion detection systems rely on CCTV footage, continuously monitoring and analyzing video feeds based on predefined rules. When an activity matches a programmed pattern, an alert is triggered. While this method provides basic security, it has notable limitations.
Despite their widespread use, rule-based detection systems have major challenges. They often suffer from high false positives and false negatives, leading to unreliable alerts. Additionally, environmental factors such as weather changes, shadows, or animal movements can cause misinterpretations. Finally, in large-scale environments, processing vast amounts of video data from multiple cameras can degrade system performance, making real-time monitoring inefficient.
To overcome these challenges, our research introduces an AI-driven intrusion detection model that improves accuracy and efficiency. In the following sections, I will explain our proposed approach in detail. |
IEEE/ICACT20250047 Slide.03
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