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To address the challenges of traditional CCTV intrusion detection, our study proposes an advanced AI-driven approach.
We implement deep learning-based object detection to accurately identify and classify objects such as people and vehicles in surveillance footage.
Additionally, we apply spatiotemporal frequency analysis to detect activity patterns over time and space.
Restricted areas are automatically designated in zones with low activity frequency, reducing manual configuration efforts and improving accuracy.
In large-scale environments with multiple cameras, real-time processing efficiency is crucial.
Our system is designed to efficiently analyze data from multiple CCTV feeds simultaneously.
By improving processing speed, we enhance detection accuracy while minimizing false alerts.
Beyond accuracy, our system focuses on user-friendly operation.
The system automatically analyzes and optimizes settings without the need for manual input.
This ensures seamless operation in large-scale surveillance environments, making security monitoring more effective and less labor-intensive.
Through this approach, we aim to develop a more reliable, scalable, and adaptive intrusion detection system that meets the demands of modern surveillance environments.
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IEEE/ICACT20250047 Slide.05
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