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Our system utilizes a grid-based activity analysis method to enhance surveillance and intrusion detection.
By dividing the surveillance area into a grid structure, we can analyze activity levels in different sections.
This approach provides clear insights into movement patterns, helping security teams focus on critical areas.
Grid cells with high object frequency indicate areas with frequent activity, such as main entrances, roads, or crowded spaces.
These zones generally require standard monitoring but may also help in detecting unusual congestion or suspicious gatherings.
In contrast, low-frequency zones represent areas with minimal movement.
Unexpected activity in these zones can indicate potential intrusions, unauthorized access, or security breaches.
By automatically detecting anomalies, the system can trigger real-time alerts to security personnel.
This grid-based approach enhances surveillance efficiency by dynamically identifying and adapting to activity patterns. Next, I will explain how our system integrates AI-driven analytics to improve detection accuracy further.
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IEEE/ICACT20250047 Slide.09
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