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Track people, vehicles, and animals as they move across your property.\n\n## What It Does\n\nInstead of getting separate \"person detected\" alerts from each camera, get one coherent narrative:\n\n> \"Man in blue jacket entered via Driveway, walked to Front Door, left package, exited via Driveway\" (2 min on property)\n\n| Traditional Alerts | Spatial Awareness |\n|-------------------|-------------------|\n| Alert per camera | One alert per movement |\n| \"Person on Camera X\" | \"Person moving from X to Y\" |\n| No identity tracking | Same object tracked across cameras |\n| Basic detection | Movement patterns, dwell time, unusual paths |\n\n## Quick Start\n\n### 1. Install\n```bash\nnpx scrypted install @blueharford/scrypted-spatial-awareness\n```\n\n### 2. Train Your Property\n\nThe fastest setup is **Training Mode** - walk your property while the system learns:\n\n1. Open plugin settings → Click **Training Mode**\n2. Tap **Start Training** on your phone\n3. Walk between cameras naturally\n4. System auto-detects you and records transit times\n5. Tap **End Training** → **Apply Results**\n\nDone! Your camera topology is configured.\n\n### 3. Configure Alerts\n\n- Select notifiers (Pushover, email, etc.)\n- Set loitering threshold (default: 3s)\n- Set per-object cooldown (default: 30s)\n\n## Features\n\n### Core\n- **Cross-Camera Tracking** - Correlate objects across cameras using timing, visual similarity, and spatial position\n- **Journey History** - Complete path for each tracked object\n- **Entry/Exit Detection** - Know when objects enter or leave your property\n- **Smart Alerts** - Loitering thresholds and per-object cooldowns prevent spam\n\n### Visual Editor\n- **Floor Plan** - Upload image or draw with built-in tools\n- **Drag & Drop** - Place cameras, draw connections\n- **Polygon Zone Drawing** - Draw custom zones (yards, driveways, patios, etc.)\n- **Live Tracking** - Watch objects move in real-time\n\n### AI Features (optional)\n- **LLM Descriptions** - \"Woman with stroller\" instead of just \"Person\"\n- **Auto-Learning** - Transit times adjust based on observations\n- **Connection Suggestions** - System suggests new camera paths\n- **Landmark Discovery** - AI identifies landmarks from footage\n- **Auto-Topology Discovery** - Vision LLM analyzes camera views to build topology\n\n### Integrations\n- **MQTT** - Home Assistant integration\n- **REST API** - Query tracked objects programmatically\n\n## Topology Configuration\n\nThe plugin uses topology data (landmarks, zones, connections) to generate meaningful alerts. Camera names are **not** used for location descriptions - only topology landmarks matter.\n\n### Setting Up Landmarks\n\nFor best alert quality, configure landmarks in the topology editor:\n\n1. **Entry/Exit Points** - Mark where people enter/exit your property\n   - Examples: `Driveway`, `Front Gate`, `Side Gate`, `Street`\n   - Set `isEntryPoint: true` or `isExitPoint: true`\n\n2. **Access Points** - Paths and walkways\n   - Examples: `Front Walkway`, `Back Path`, `Garage Door`\n   - Type: `access`\n\n3. **Zones** - Areas of your property\n   - Examples: `Front Yard`, `Back Yard`, `Side Yard`, `Patio`\n   - Type: `zone`\n\n4. **Structures** - Buildings and fixed features\n   - Examples: `Garage`, `Shed`, `Front Porch`, `Deck`\n   - Type: `structure`\n\n5. **Features** - Other notable landmarks\n   - Examples: `Mailbox`, `Pool`, `Garden`, `Trash Cans`\n   - Type: `feature`\n\n### Linking Landmarks to Cameras\n\nEach camera should have `visibleLandmarks` configured - the landmarks visible in that camera's view:\n\n```json\n{\n  \"cameras\": [{\n    \"deviceId\": \"abc123\",\n    \"name\": \"Front Camera\",\n    \"context\": {\n      \"visibleLandmarks\": [\"front-door\", \"driveway\", \"mailbox\"]\n    }\n  }]\n}\n```\n\n### Example Topology\n\n```json\n{\n  \"landmarks\": [\n    { \"id\": \"driveway\", \"name\": \"Driveway\", \"type\": \"access\", \"isEntryPoint\": true },\n    { \"id\": \"front-door\", \"name\": \"Front Door\", \"type\": \"access\" },\n    { \"id\": \"backyard\", \"name\": \"Back Yard\", \"type\": \"zone\" },\n    { \"id\": \"garage\", \"name\": \"Garage\", \"type\": \"structure\" }\n  ]\n}\n```\n\nWith proper landmarks, alerts become rich and contextual:\n- \"Person arrived at the Driveway from Main Street\"\n- \"Person moved from the Front Porch heading towards the Back Yard\"\n- \"Person left the Garage towards Driveway after 2m on property - visited Driveway > Front Door > Garage\"\n\n## Configuration\n\n### Settings\n\n| Setting | Default | Description |\n|---------|---------|-------------|\n| Correlation Window | 30s | Max time for cross-camera matching |\n| Correlation Threshold | 0.35 | Min confidence for auto-correlation |\n| Loitering Threshold | 3s | Time before triggering alerts |\n| Minimum Detection Confidence | 0.5 | Ignore detections below this score |\n| Per-Object Cooldown | 30s | Min time between alerts for same object |\n| Notify on Alert Updates | Off | Send notifications when an existing alert updates |\n| Alert Update Cooldown | 60s | Min time between update notifications |\n| LLM Rate Limit | 5s | Min time between LLM API calls |\n\n### Alert Types\n\n| Alert | Description |\n|-------|-------------|\n| Property Entry | Object entered via entry point |\n| Property Exit | Object exited via exit point |\n| Movement | Object moved between cameras |\n| Unusual Path | Object took unexpected route |\n| Dwell Time | Object lingered >5 minutes |\n\n## API\n\nBase URL: `/endpoint/@blueharford/scrypted-spatial-awareness`\n\n| Endpoint | Method | Description |\n|----------|--------|-------------|\n| `/api/tracked-objects` | GET | List tracked objects |\n| `/api/journey/{id}` | GET | Get object journey |\n| `/api/topology` | GET/PUT | Camera topology |\n| `/api/alerts` | GET | Recent alerts |\n| `/api/live-tracking` | GET | Real-time object positions |\n| `/ui/editor` | GET | Visual topology editor |\n| `/ui/training` | GET | Training mode UI |\n\n### Training API\n\n| Endpoint | Method | Description |\n|----------|--------|-------------|\n| `/api/training/start` | POST | Start training session |\n| `/api/training/end` | POST | End session, get results |\n| `/api/training/apply` | POST | Apply results to topology |\n| `/api/training/status` | GET | Current training status |\n\n### Discovery API\n\n| Endpoint | Method | Description |\n|----------|--------|-------------|\n| `/api/discovery/scan` | POST | Run full discovery scan |\n| `/api/discovery/status` | GET | Current discovery status |\n| `/api/discovery/suggestions` | GET | Pending suggestions |\n| `/api/discovery/camera/{id}` | GET | Analyze single camera |\n\n## Auto-Topology Discovery\n\nThe plugin can automatically analyze camera views using a vision-capable LLM to discover landmarks, zones, and camera connections.\n\nIf a configured LLM provider does not support vision, it will be automatically excluded from discovery and shown in plugin settings under **Excluded LLMs (No Vision)**.\n\n### How It Works\n\n1. **Capture Snapshots** - System takes a picture from each camera\n2. **Scene Analysis** - Vision LLM identifies landmarks, zones, and edges in each view\n3. **Cross-Camera Correlation** - LLM correlates findings across cameras to identify shared landmarks and connections\n4. **Suggestions** - Discoveries are presented as suggestions you can accept or reject\n\n### Using Discovery\n\n**Manual Scan:**\n1. Open the topology editor (`/ui/editor`)\n2. Find the \"Auto-Discovery\" section in the sidebar\n3. Click \"Scan Now\"\n4. Review and accept/reject suggestions\n\n**Automatic Scan:**\n- Set `Auto-Discovery Interval (hours)` in plugin settings\n- System will periodically scan and generate suggestions\n- Set to 0 to disable automatic scanning\n\n### Discovery Settings\n\n| Setting | Default | Description |\n|---------|---------|-------------|\n| Auto-Discovery Interval | 0 (disabled) | Hours between automatic scans (0 = disabled) |\n| Min Landmark Confidence | 0.6 | Minimum confidence for landmark suggestions |\n| Min Connection Confidence | 0.5 | Minimum confidence for connection suggestions |\n| Auto-Accept Threshold | 0.85 | Auto-accept suggestions above this confidence |\n\n> **Rate Limiting Note:** If you set the interval to less than 1 hour, a warning will appear in the discovery status. Frequent scans can consume significant LLM API quota and may be rate-limited by your provider.\n\n### Requirements\n\n- **Vision-capable LLM** - Install @scrypted/llm with a vision model (OpenAI GPT-4V, Claude, etc.)\n- **Camera access** - Plugin needs camera.takePicture() capability\n\n### What Gets Discovered\n\n- **Landmarks**: Doors, gates, mailbox, garage, structures, fences\n- **Zones**: Front yard, driveway, patio, street, walkways\n- **Connections**: Suggested camera paths with transit time estimates\n- **Edges**: What's visible at frame boundaries (for correlation)\n\n## Zone Drawing\n\nThe visual editor includes a polygon zone drawing tool for marking areas on your floor plan.\n\n### How to Draw Zones\n\n1. Click the **Draw Zone** button in the toolbar (green)\n2. Enter a zone name and select the type (yard, driveway, patio, etc.)\n3. Click **Start Drawing**\n4. Click on the canvas to add polygon points\n5. **Double-click** or press **Enter** to finish the zone\n6. Press **Escape** to cancel, **Backspace** to undo last point\n\n### Zone Types\n\n| Type | Color | Description |\n|------|-------|-------------|\n| Yard | Green | Front yard, backyard, side yard |\n| Driveway | Gray | Driveway, parking area |\n| Street | Dark Gray | Street, sidewalk |\n| Patio | Orange | Patio, deck |\n| Walkway | Brown | Walkways, paths |\n| Parking | Light Gray | Parking lot, parking space |\n| Garden | Light Green | Garden, landscaped area |\n| Pool | Blue | Pool area |\n| Garage | Medium Gray | Garage area |\n| Entrance | Pink | Entry areas |\n| Custom | Purple | Custom zone type |\n\n### Using Zones\n\n- Click on a zone to select it and edit its properties\n- Zones are color-coded by type for easy identification\n- Zones help provide context for object movement descriptions\n- Auto-Discovery can suggest zones based on camera analysis\n\n## MQTT Topics\n\nBase: `scrypted/spatial-awareness`\n\n| Topic | Description |\n|-------|-------------|\n| `/occupancy/state` | ON/OFF property occupancy |\n| `/count/state` | Active object count |\n| `/person_count/state` | People on property |\n| `/alerts` | Alert events |\n| `/events/entry` | Entry events |\n| `/events/exit` | Exit events |\n\n## How Correlation Works\n\nWhen an object is detected on a new camera, the system scores potential matches:\n\n| Factor | Weight | Description |\n|--------|--------|-------------|\n| Timing | 30% | Transit time within expected range |\n| Visual | 35% | Embedding similarity (if available) |\n| Spatial | 25% | Exit zone → Entry zone coherence |\n| Class | 10% | Object type match |\n\nObjects are correlated if total score exceeds threshold (default: 0.6).\n\n## Requirements\n\n- Scrypted with NVR plugin\n- Cameras with object detection (NVR, OpenVINO, CoreML, etc.)\n- Optional: LLM plugin for rich descriptions\n- Optional: MQTT broker for Home Assistant\n\n## Development\n\n```bash\nnpm install\nnpm run build\nnpm run scrypted-deploy\n```\n\n## License\n\nApache-2.0\n\n## Author\n\nJoshua Seidel ([@blueharford](https://github.com/blueharford))\n","readmeFilename":"README.md"}