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Our proposed model integrates real-time data acquisition with AI-based object detection to improve intrusion detection accuracy and efficiency.
The system uses RTSP for real-time CCTV data streaming, applies YOLO for object detection, and automatically designates low-activity areas as restricted zones.
First, we use the Real-Time Streaming Protocol (RTSP) to efficiently acquire video data from CCTV cameras.
RTSP is a standardized protocol designed for efficient video and audio streaming.
It is compatible with various CCTV models and brands, allowing seamless integration with existing security systems without additional hardware.
The system supports multiple camera streams, making it ideal for large-scale environments like airports, malls, and industrial sites.
Additionally, low-latency processing ensures real-time intrusion detection, which is critical for immediate threat response.
To enhance detection accuracy, we implement YOLO (You Only Look Once), a deep learning-based object detection algorithm.
Unlike traditional detection methods, YOLO processes entire images in a single pass, allowing real-time identification of objects such as people, vehicl.
Positional data is captured to track movement and assess security threats, distinguishing between intruders, authorized personnel, and animal.
This approach reduces false positives and improves detection accuracy while enabling behavior analysis, crowd monitoring, and anomaly detection for advanced surveillance capabilities.
By integrating real-time streaming with deep learning, our model enhances security operations by reducing manual effort and improving detection precision. Next, I will discuss how the system analyzes activity patterns over time and space to optimize surveillance further.
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IEEE/ICACT20250047 Slide.06
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