Intrusion Detection with Computer Vision

What is Intrusion Detection?
Intrusion detection systems are an integral part of most physical security systems, critical infrastructure protection, and safety applications in transportation and manufacturing.
Modern monitoring of specified areas uses computer vision, specifically deep learning algorithms, to automatically detect intrusion events. AI vision-based event detection and recognition are applied using the real-time video stream of common CCTV surveillance cameras.
Key Features of Intrusion Detection
State-of-the-art methods for intrusion detection can detect anomalies using real-time activity recognition.
Real-time object detection algorithms to detect people and objects in the video of multiple cameras (high scalability).
The regions of interest can be visually determined by drawing and naming specific areas within camera frames.
Custom logic is developed to trigger alerts based on how objects are detected within those areas (for example, after a person enters an area for more than 5 seconds).
Edge AI with on-device machine learning allows privacy-preserving, highly performant, and robust (offline capabilities) perimeter detection systems.
Value of Intrusion Detection with Deep Learning
Computer vision perimeter monitoring with intrusion detection is fully automated and highly scalable across many cameras with computing endpoints to process the visuals on-device (Edge AI).
Fully automated perimeter control with intrusion recognition saves costs of manual personnel required.
Deep learning provides perimeter security systems with the best recognition accuracy among all comparative methods.
Higher accuracy compared to traditional methods, with more information that can be extracted using AI.
Enhanced safety and security through large-scale machine learning-based anomaly detection.
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