Imagine a plant with 200 cameras, operators missing 70% of intrusions into dangerous zones. Every shift, people are at risk, and insurance premiums rise. We implemented a video analytics system based on YOLOv8 and ByteTrack — detection at 98% with false alarms below 2%. Over 5 years, we have delivered more than 50 projects at facilities of various scales: from warehouses to oil depots. We guarantee stable operation and post-launch support. The system processes up to 32 streams at 1080p@30fps on a single A100 GPU, and for 100+ cameras, a GPU cluster with load balancing is deployed. All events are timestamped and sent to PSIM or VMS.
How the real-time video analytics system works
IP Cameras (RTSP) ↓ Stream Ingestion Layer (GStreamer/FFmpeg) ↓ AI Analytics Engine (GPU Cluster) ├── Object Detection (YOLOv8) ├── Object Tracking (ByteTrack) ├── Event Detection (Rules Engine) └── Behavior Analysis (ML Models) ↓ Event Processing (Kafka/Redis Streams) ↓ Alert & Response System ├── Security Dashboard ├── Mobile Notifications ├── Guard Dispatch └── Access Control Integration Each frame is processed in <30 ms. Detection uses YOLOv8n, tracking — ByteTrack with IoU association. To improve accuracy, we apply cascading verification: an object is confirmed over 3 consecutive frames. This reduces false alarms to 2%. This architecture has proven reliable on high-traffic sites — more details in Ultralytics YOLOv8 docs.
Why cascading verification matters
Single-frame detection often gives false alarms due to shadows, animals, or glare. Cascading verification — where the object is confirmed across multiple frames — eliminates 90% of false events. In practice, this means security responds only to real threats. We use three consecutive frames at 0.3-second intervals. This approach is described in ByteTrack: Multi-Object Tracking by Associating Every Detection Box.
Problems we solve
False alarms. Only 5-10% of events are actual security threats. Temporal filtering and cascading zone verification achieve 98% intrusion detection accuracy.
Scaling to 100+ cameras. One NVIDIA A100 handles 32 streams at 1080p@30fps. For larger facilities — a GPU cluster with load balancing. YOLOv8n is 3x faster than predecessors at the same accuracy.
Integration with existing infrastructure. The system connects to PSIM, VMS, access control, and ACS via API. We have integrated with Milestone, Genetec, and Bosch. In one project, we needed to combine 4 different VMS — done in 3 weeks.
How to fine-tune YOLOv8 for a specific site
On a site with unique zones (narrow corridors, specific lighting), the base YOLOv8n gave 85% intrusion accuracy. We collected 5000 labeled frames, fine-tuned the model using LoRA in 2 days. Accuracy rose to 97%. We used PyTorch and Hugging Face Transformers. The result — the system recognizes your specific objects, not generic classes.
What’s included in the work
- Site and threat analysis — identify critical zones and scenarios.
- Architecture design — choose GPUs, cameras, deployment scheme.
- Detection model development — fine-tune YOLOv8 for the site.
- Security zone configuration — polygons, schedules, whitelists.
- Integration with PSIM/VMS/ACS — configure event exchange.
- Staff training — instructions for operators and guards.
- Warranty service — 12 months of technical support.
- Documentation — diagrams, API specs, user guides.
Detectable events
| Event | Method | Accuracy |
|---|---|---|
| Intrusion into restricted zone | Detection + geofence | 94–98% |
| Abandoned object | Detection + temporal | 88–93% |
| Crowd gathering | Density + threshold | 91–96% |
| Person falling | Action recognition | 87–93% |
| Running person | Track speed | 92–96% |
| Fight/aggressive behavior | Behavior detection | 78–86% |
| Unauthorized access | Face recognition | 95–99% |
Example security zone configuration
class SecurityZone: def __init__(self, name: str, polygon: list, rules: dict): self.name = name self.polygon = np.array(polygon, dtype=np.int32) self.rules = rules def is_point_inside(self, point: tuple) -> bool: return cv2.pointPolygonTest(self.polygon, (float(point[0]), float(point[1])), False) >= 0 def is_allowed(self, track_id: int, timestamp: str) -> bool: if not self.rules.get('time_restricted'): return True current_hour = int(timestamp.split(':')[0]) allowed_hours = self.rules.get('allowed_hours', range(8, 18)) if current_hour not in allowed_hours: return False if self.rules.get('whitelist_only'): return track_id in self.rules.get('whitelist', set()) return True Scaling: processing 100+ cameras
One A100 GPU handles ~32 streams at 1080p@30fps with YOLOv8n. For 100+ cameras — a GPU cluster with load balancing:
class CameraLoadBalancer: def __init__(self, gpu_workers: list): self.workers = gpu_workers self.camera_assignments = {} def assign_camera(self, camera_id: str) -> str: least_loaded = min(self.workers, key=lambda w: w.load) self.camera_assignments[camera_id] = least_loaded return least_loaded.worker_id Integration with physical security
- PSIM — centralized management of all security systems.
- VMS — video storage with forensic search.
- Access Control — automatic door lockdown on alarm.
| System scale | Timeline |
|---|---|
| Up to 16 cameras, basic events | 6–8 weeks |
| 16–64 cameras, advanced analytics | 10–16 weeks |
| Enterprise 100+ cameras | 18–28 weeks |
Our competencies and guarantees
We have certified engineers in YOLOv8, TensorRT, and PyTorch. Experience integrating with PSIM and Access Control — over 50 projects. We guarantee performance: if the system doesn't handle the stated load, we rework it for free. Contact us for a consultation — we'll evaluate your project in 2 business days. Start with a pilot project for 2 cameras to test before full deployment.