Imagine: 64 cameras on a site — and not a single glance at the monitor. A security guard can't keep track of all streams physically. AI-based video analytics turns passive recording into active monitoring. We develop such systems turnkey: from intrusion detection to anomaly behavior analysis. Our engineers hold NVIDIA certifications and have years of experience in Computer Vision. We assess the task in 2 business days; basic system timeline starts from 4 weeks. The average payback period is 12–18 months. For a 64-camera site, security cost savings reach 1.2 million rubles per year. Order a site audit — we'll select the optimal solution. Get a consultation right now.
Why AI Video Analytics Is More Profitable Than Traditional Surveillance
Classic CCTV costs money but does nothing. 64 cameras mean 64 monitors that no one watches 96% of the time. AI analytics transitions the system from passive recording to active monitoring: the camera itself reports when something goes wrong. This reduces security costs by up to 40%, speeding up incident response to a few seconds.
Key Modules of AI Video Analytics
Intrusion Detection and Perimeter Crossing
The basic module of any video analytics system. Technically — people detection (YOLOv8, RT-DETR) plus crossing of a virtual line or zone. The difficulty is not in detection itself but in minimizing false alarms. Typical causes of false positives: animals, shadows, headlight glare, rain. Solution: a person vs. non-person classifier with confidence threshold 0.75+, temporal filtering (the object must be present for at least N consecutive frames), ROI masks to exclude noisy areas. On a calibrated system, false alarm rate < 0.3 per camera per hour. We guarantee stability — SLA 99.9%. More about YOLO on Wikipedia.
How Multi-Camera Tracking Works in Real Projects
On a large site (shopping mall, factory, airport), it's more interesting not "what a person does on one camera" but "where they go across the whole site." Multi-Camera Multi-Object Tracking (MCMOT) is one of the actively researched CV tasks.
Two approaches:
- Appearance-based re-id: extracting an appearance descriptor from each camera (BoT-BOT, OSNet, SBS-R101 from Fast-ReID), matching by cosine similarity. Works well with non-overlapping fields of view.
- Topology-aware matching: using transition topology between cameras (we know that from zone A a person can get to zone B in 30–120 seconds). Reduces false matches on similar people.
Case study: a warehouse complex of 45,000 m², 128 Axis IP cameras. Task — monitoring compliance of personnel routes in restricted access areas. DeepSORT → FastReID pipeline, processing on a server with 4× NVIDIA RTX 4090. End-to-end tracking time of one person through 15 cameras: < 800 ms latency from real time. NVIDIA Jetson AGX Orin handles 8–16 HD streams onboard, which is 3 times more efficient than server solutions for edge processing.
Deep Dive: Anomalous Behavior Detection
This is the most technically complex and valuable module. Any YOLOv8 can detect a person. Understanding that they are doing something suspicious is a fundamentally different task.
What Is Considered an Anomaly
Anomalous behavior is a statistically rare or contextually unexpected action: a person falls, drops an object, leaves luggage, aggressive movement, crowd gathering, movement in an atypical direction.
Detection Approaches
| Method | Principle | When Applicable | False Positive Rate |
|---|---|---|---|
| Action recognition (SlowFast, Video Swin) | Classification of actions from 2–4 second clips | Clearly defined events | Low on trained actions |
| Anomaly detection (Conv-AE, PatchCore) | Reconstruction error on "normal" scenes | Atypical situations without labeling | High in dynamic scenes |
| Trajectory analysis (KDE) | Density of tracks in space-time | Crowds, falls | Medium |
In practice, we combine: rule-based detection for well-defined events (line crossing, forbidden zone) plus anomaly detection for atypical situations.
AI Video Surveillance Architecture
Edge component: processing video streams close to cameras reduces network load. NVIDIA Jetson AGX Orin handles 8–16 HD streams with detection and tracking. NVIDIA DeepStream and TensorRT are the standard stack.
Server component: video archive storage, event analytics, management. VMS (Video Management System): Milestone XProtect, Genetec Security Center, or open source — Frigate NVR. Storage — object storage (MinIO or S3), only analytically significant clips, not the entire stream.
Tools: NVIDIA DeepStream, OpenCV, ByteTrack, Fast-ReID, MLflow for model versioning.
| Component | Recommended Solution | Alternative |
|---|---|---|
| People detection | YOLOv8m TensorRT | RT-DETR |
| Tracking | ByteTrack | BoT-SORT |
| Re-ID | OSNet (Fast-ReID) | SBS-R50 |
| VMS | Milestone XProtect | Frigate NVR |
| Edge platform | Jetson AGX Orin | Intel NUC + iGPU |
More on technical specifications
The system supports up to 128 cameras per server, latency p99 does not exceed 500 ms for detection and tracking. We use INT8 quantization for TensorRT, increasing throughput by 30% without accuracy loss. For edge deployment, we use Jetson AGX Orin with 275 TOPS.How We Implement AI Video Analytics: Step-by-Step Process
- Site audit: survey the territory, capture plans, gather requirements. Determine the number of cameras, areas of interest, integration points.
- Design: choose architecture (edge/cloud/hybrid), select detection and tracking models, configure DeepStream settings.
- Development and calibration: collect data from the site, annotate (YOLO format), fine-tune models, set up ROI masks and temporal filtering.
- Integration: connect to VMS, configure alerts, develop user interface (event dashboard).
- Testing: trial run on real streams, measure false alarm rate, adjust thresholds.
- Deployment and training: deploy on equipment, train operators, hand over documentation.
- Warranty support: 6 months of monitoring, SLA 99.9%, re-training models if necessary.
What's Included in the Work
- Architecture description and model specification
- Integration and API documentation
- Training of system operators
- Test period with calibration for the site
- 6-month warranty support and SLA 99.9%
Timelines
Basic intrusion detection system for one site: 4–6 weeks. Full video analytics with tracking, behavioral analysis, and VMS integration: 3–5 months. Cost is calculated individually based on scope.
We have completed over 10 projects in retail and industry. Want to assess your task? Get a consultation — we'll prepare an estimate in 2 business days. Order a site audit, and we'll suggest the optimal solution.