Getting Started Reference¶
Quick reference for new users of Home Security Intelligence.
This page provides a quick overview and links to the detailed getting started documentation.
Quick Links¶
| Guide | Description |
|---|---|
| Prerequisites | Hardware and software requirements |
| Installation | Step-by-step installation guide |
| First Run | Starting the system for the first time |
| Quick Start | Get up and running in 5 minutes |
| Dashboard Tour | Interactive tour of the dashboard |
| Upgrading | Upgrade from previous versions |
System Overview¶
Home Security Intelligence is an AI-powered home security monitoring dashboard that:
- Processes camera feeds using YOLO26 object detection
- Analyzes detections with Nemotron LLM for risk assessment
- Provides real-time alerts via WebSocket connections
- Tracks entities (people, vehicles, pets) across cameras
- Maintains activity baselines for anomaly detection
Architecture Components¶
Camera Images
|
v
+-------------+ +-------------+ +-------------+
| YOLO26 |----->| Enrichment |----->| Florence-2 |
| (8095) | | (8094) | | (8092) |
+-------------+ +-------------+ +-------------+
| | |
v v v
+-----------------------------------------------------------+
| Nemotron (8091) |
| Risk Analysis & Scoring |
+-----------------------------------------------------------+
|
v
Risk Events
Minimum Requirements¶
Hardware¶
| Component | Minimum | Recommended |
|---|---|---|
| GPU | 8 GB VRAM | 24 GB VRAM |
| RAM | 16 GB | 32 GB |
| Storage | 100 GB SSD | 500 GB NVMe |
| CPU | 4 cores | 8+ cores |
Software¶
| Component | Version |
|---|---|
| Python | 3.14+ |
| Node.js | 20+ |
| Container Runtime | Docker/Podman |
| CUDA | 12.0+ |
Quick Start (5 Minutes)¶
# 1. Clone repository
git clone https://github.com/your-org/home-security-intelligence.git
cd home-security-intelligence
# 2. Run setup
./setup.sh
# 3. Download AI models
./ai/download_models.sh
# 4. Start services
podman-compose -f docker-compose.prod.yml up -d
# 5. Open dashboard
open http://localhost:3000
Key Concepts¶
Risk Scoring¶
The system assigns risk scores (0-100) to events:
| Score | Level | Description |
|---|---|---|
| 0-29 | Low | Normal activity, no concern |
| 30-59 | Medium | Unusual but not alarming |
| 60-84 | High | Requires attention |
| 85-100 | Critical | Immediate investigation recommended |
Detection Classes¶
YOLO26 detects security-relevant objects:
SECURITY_CLASSES = {
"person", "car", "truck", "dog", "cat",
"bird", "bicycle", "motorcycle", "bus"
}
Entity Types¶
The system tracks these entity types:
| Type | Description |
|---|---|
| person | Human individuals |
| vehicle | Cars, trucks, motorcycles |
| animal | Pets and wildlife |
| package | Delivered packages |
| other | Unclassified objects |
Keyboard Navigation¶
Press ? anywhere to see keyboard shortcuts.
| Action | Shortcut |
|---|---|
| Command palette | Cmd/Ctrl + K |
| Go to Dashboard | g d |
| Go to Timeline | g t |
| Help | ? |
See Keyboard Shortcuts for complete reference.
Configuration Files¶
| File | Purpose |
|---|---|
.env | Environment configuration |
docker-compose.prod.yml | Container orchestration |
config/gpu-assignments.yml | GPU assignments |
pyproject.toml | Python dependencies |
Common Tasks¶
View Live Events¶
- Open Dashboard (
g d) - Activity feed shows real-time detections
- Click event for details
Check System Health¶
- Navigate to System page (
g y) - View GPU utilization, service status
- Check AI model performance
Configure Cameras¶
- Go to Settings (
g s) - Camera Management section
- Add FTP upload paths
Review Analytics¶
- Open Analytics (
g n) - View detection trends
- Filter by date range
Troubleshooting¶
| Issue | Solution |
|---|---|
| No detections appearing | Check YOLO26 service: curl localhost:8095/health |
| High risk scores | Review Nemotron prompts and thresholds |
| GPU out of memory | Reduce batch size or use smaller model |
| WebSocket disconnects | Check Redis connection and backend logs |
See Troubleshooting Guide for detailed solutions.
Next Steps¶
After initial setup:
- Configure zones - Set up detection zones for each camera
- Add household members - Register known people for smart alerts
- Set up notifications - Configure webhook integrations
- Review baselines - Let the system learn normal patterns (7 days)