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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.


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

  1. Open Dashboard (g d)
  2. Activity feed shows real-time detections
  3. Click event for details

Check System Health

  1. Navigate to System page (g y)
  2. View GPU utilization, service status
  3. Check AI model performance

Configure Cameras

  1. Go to Settings (g s)
  2. Camera Management section
  3. Add FTP upload paths

Review Analytics

  1. Open Analytics (g n)
  2. View detection trends
  3. 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:

  1. Configure zones - Set up detection zones for each camera
  2. Add household members - Register known people for smart alerts
  3. Set up notifications - Configure webhook integrations
  4. Review baselines - Let the system learn normal patterns (7 days)


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