AI Physical Security System Development: Turnkey
We develop turnkey AI physical security systems for industrial sites, data centers, and offices. Standard CCTV cameras without analytics are incident archives, not prevention systems. Security guards cannot physically monitor 80 cameras simultaneously, so threats are missed. Our solution is turnkey, integrating into your existing infrastructure. We assess your project in 2 days. Our track record: over 15 deployments, detection accuracy 95%+ with false alarms under 5%.
AI Tasks in Physical Security
Access control. Face verification for turnstile passage without cards or PIN. This is not face identification in public spaces but strict verification against an authorized list.
Intrusion detection. Real-time video stream analysis: human in restricted area, movement during off-hours. Trigger is not motion (otherwise it would react to leaves) but semantically significant events.
Anomaly behavior detection. Person leaving an object, falling, aggressive gestures, unusual crowding.
PPE control. On production floors: missing helmet, vest, gloves. YOLOv8 models with custom dataset. Sensitive area access monitoring. Server rooms, storage — tailgating detection and people counting.
How AI Detects Tailgating?
Tailgating is when a second person passes through a controlled door "piggybacking" on the first, without authentication. Standard sensors are unreliable. Computer vision approach: door opening detection, people tracking via pose estimation (MediaPipe or ViTPose), matching authentication events with passages. If 2 people pass through the door but only 1 authentication — alert. Lab accuracy: 97%. In real conditions (variable lighting, occlusion): 88–92%. False alarm rate after calibration: 2–4%.
Why Edge Processing is More Efficient than Cloud?
For tasks with latency <1 second (intrusion, PPE), inference on edge (NVIDIA Jetson Orin) gives minimal delay. Cloud is for post-event analytics and storage. Example: YOLOv8n in INT8 via TensorRT on Jetson — 45–60 FPS at 15W. The same model in FP32 without optimization — 12 FPS. Factor of 3.75 difference.
| Approach | Latency | Performance | Power Consumption |
|---|---|---|---|
| Edge (Jetson Orin, INT8) | <100 ms | 45–60 FPS | 10–15 W |
| Cloud (GPU) | 300–500 ms | 60+ FPS | 100+ W (excluding network) |
Technical Architecture of Video Analytics
Processing 64 cameras 1080p@25fps imposes strict latency and resource requirements.
Model optimization. YOLOv8n/YOLOv9 in INT8 quantization via TensorRT on Jetson: 45–60 FPS on 1080p at 10–15W. Without optimization the same model in FP32 runs at 12 FPS.
import tensorrt as trt def optimize_for_jetson(onnx_path: str) -> trt.ICudaEngine: builder = trt.Builder(trt.Logger(trt.Logger.WARNING)) config = builder.create_builder_config() config.set_flag(trt.BuilderFlag.INT8) config.int8_calibrator = CalibrationDataset(calibration_data) network = builder.create_network( 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH) ) parser = trt.OnnxParser(network, trt.Logger()) parser.parse_from_file(onnx_path) return builder.build_engine(network, config) Multi-camera tracking. Person re-identification (Re-ID) tracks a person across cameras without overlap. Backbone: OSNet or TransReID. Similarity search via embeddings in real-time using FAISS index.
What's Included in AI System Development?
- Site audit: threat types, number of cameras, latency requirements
- Model and architecture selection (edge/cloud)
- Dataset collection and labeling for the specific site
- Training, quantization (INT8/FP16), deployment on Jetson
- Sensitivity zone calibration and threshold setting
- Integration with ACS, notification system
- Staff training, documentation
- 24/7 technical support for first 2 months
Practical Case
Our client — a data center with 180 cameras, 3 guards per shift. After AI analytics deployment:
- 99.7% of the time system operated autonomously, guards reacted only to alerts
- Average incident response time: 23 seconds (previously missed)
- 12 prevented unauthorized accesses in 6 months
- Two tailgating incidents that were not detected before
- False alarms: 1.8/day — acceptable for security
Key point: first 2 weeks calibrating sensitivity zones. Without that — hundreds of alerts on cleaners and lighting. After calibration — precise operation.
Privacy Considerations
Face recognition requires a legal basis: employee consent or employment contract for internal areas. In public spaces — special permission. Biometric data handling complies with local data protection regulations: encrypted storage, access control. Our team ensures compliance.
Our Competencies
- 5+ years of experience in Computer Vision and MLOps
- 15+ deployed AI security systems
- Certified NVIDIA Jetson and TensorRT engineers
Contact us for your project assessment. Get a consultation on implementing AI security tailored to your budget.