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Taiwan Highway CCTV Viewer with Real-Time Vehicle Detection

Real-time viewer for 2,500+ Taiwan Highway Bureau CCTV feeds with YOLOv8 vehicle detection, tracking, database storage, WebSocket updates, and Stream Out integration.

Features

Backend

  • YOLOv8 Vehicle Detection: Cars, buses, trucks at 60% confidence with bounding boxes
  • Object Tracking: Multi-object tracking with unique IDs and vehicle counting
  • Database: SQLite storage for detections, tracks, and historical data
  • WebSocket: Real-time push notifications for vehicle detections
  • Stream Out Integration:
    • CoT (Cursor on Target) via UDP for TAK/ATAK
    • Lattice (Anduril) via REST API
  • Performance Optimizations:
    • Concurrent ingestion with connection pooling
    • Selective YOLO processing (skip unchanged frames)
    • Async operations throughout
    • In-memory caching
  • REST API: Comprehensive endpoints for feeds, stats, search, and map data
  • Operational Monitoring:
    • Health check endpoints for all system components
    • Prometheus metrics export for performance monitoring
    • Structured JSON logging for debugging
    • Circuit breaker pattern for graceful degradation
    • Alert management system for critical events
    • Real-time operational dashboard UI

Frontend

  • Grid View (index.html)

    • Real-time vehicle detection badges ("TAI OCCUPIED")
    • Auto-refresh thumbnails (2 second interval)
    • Filter by status (All/Working Only/Vehicles Detected)
    • Search by location/road/ID
    • Stream Out configuration UI
    • WebSocket real-time updates

  • Map View (map.html)

    • Interactive Leaflet map with all camera locations
    • Color-coded markers (green=online, red=vehicles, gray=offline)
    • Marker clustering for performance
    • Real-time marker updates via WebSocket
    • Filter by road name and vehicle detection
    • Click markers for live snapshots and details

  • Single Feed View (feed.html)

    • High-frequency updates (500ms)
    • Full camera details and location
    • Vehicle detection indicators

  • Monitoring Dashboard (monitoring.html)

    • Real-time system health visualization (auto-refresh every 5 seconds)
    • Component status monitoring (Database, YOLO, HTTP client, WebSocket, Cache, Tracker, Feed source)
    • System metrics (CPU, memory, disk usage)
    • Feed statistics with availability progress bars
    • Performance metrics (cache size, active connections, circuit breaker state)
    • Database statistics (detection count, track count)
    • Recent alerts display with severity levels
    • Database reset functionality
    • Dark theme responsive UI

Architecture

┌─────────────────┐         ┌────────────────────────────┐         ┌─────────────────┐
│   Taiwan        │         │  FastAPI Backend (8001)    │         │   Web Client    │
│   Highway       │────────▶│  - YOLOv8n Detector        │◀───────▶│   (Browser)     │
│   Bureau        │  HTTPS  │  - Object Tracker          │  HTTP   │   Port 8000     │
│   (2500 feeds)  │         │  - SQLite Database         │  + WS   │                 │
└─────────────────┘         │  - WebSocket Manager       │         └─────────────────┘
                            │  - Memory Cache            │
                            └────────────────────────────┘
                                       │
                                       │ Concurrent Ingestion
                                       │ (Batches of 240)
                                       │
                                       ▼
                            ┌──────────────────┐
                            │   SQLite DB      │
                            │  - detections    │
                            │  - tracks        │
                            │  - feeds         │
                            └──────────────────┘
                                       │
                                       ▼
                            ┌──────────────────┐
                            │   Stream Out     │
                            │   (Optional)     │──UDP──▶ TAK/ATAK (CoT)
                            │                  │
                            │                  │──HTTPS─▶ Lattice (Anduril)
                            └──────────────────┘

Quick Start

Automatic (Recommended)

./start.sh

Then open: http://localhost:8000

The startup script will:

  1. Create/activate virtual environment
  2. Install all dependencies (including YOLOv8)
  3. Start backend server on port 8001
  4. Start frontend server on port 8000
  5. Handle graceful shutdown with Ctrl+C

Access the monitoring dashboard: http://localhost:8000/monitoring.html

Manual Setup

Terminal 1 - Backend:

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
python backend/main.py

Terminal 2 - Frontend:

cd client
python3 -m http.server 8000

Then open: http://localhost:8000

API Endpoints

Health & Monitoring Endpoints

GET /health

Comprehensive health check for all system components

Response:

{
  "status": "healthy",
  "timestamp": "2025-11-21T10:30:00Z",
  "components": [
    {
      "component": "database",
      "status": "healthy",
      "message": "Database connection healthy",
      "latency_ms": 1.2
    },
    {
      "component": "yolo_model",
      "status": "healthy",
      "message": "YOLO model loaded and ready"
    }
  ],
  "system": {
    "cpu_percent": 25.3,
    "memory": {
      "total_gb": 16.0,
      "used_gb": 8.5,
      "percent": 53.1
    },
    "disk": {
      "total_gb": 500.0,
      "used_gb": 250.0,
      "percent": 50.0
    }
  }
}

Components monitored:

  • Database connectivity & performance
  • YOLO model availability
  • HTTP client health
  • WebSocket manager status
  • Cache health & size
  • Vehicle tracker status
  • Feed source availability

GET /health/live

Kubernetes liveness probe - simple check if server is running

Response: {"status": "alive", "timestamp": "..."}

GET /health/ready

Kubernetes readiness probe - check if system is ready for traffic

Response: {"status": "ready", "timestamp": "..."} (200 if ready, 503 if not)

GET /metrics

Prometheus metrics export endpoint for monitoring and alerting

Metrics categories:

  • Detection metrics: cctv_detections_total, cctv_detection_confidence, cctv_tracks_total
  • Performance metrics: cctv_yolo_inference_seconds, cctv_feed_fetch_seconds, cctv_cycle_duration_seconds
  • System metrics: cctv_feeds_total, cctv_feeds_online, cctv_cache_size_bytes, cctv_active_websockets
  • Error metrics: cctv_errors_total, cctv_feed_failures_total

Example queries:

# Feed availability percentage
(cctv_feeds_online / cctv_feeds_total) * 100

# Detection rate per minute
rate(cctv_detections_total[1m])

# P95 YOLO inference time
histogram_quantile(0.95, rate(cctv_yolo_inference_seconds_bucket[5m]))

GET /api/operational/status

Operational dashboard data endpoint

Response:

{
  "uptime_seconds": 3600,
  "uptime_human": "1h 0m",
  "feeds": {
    "total": 2402,
    "online": 1876,
    "offline": 526,
    "online_percentage": 78.1,
    "with_vehicles": 145
  },
  "cache": {
    "size_mb": 450.5,
    "items": 2402,
    "avg_size_kb": 192.0
  },
  "components": {
    "yolo_model": "healthy",
    "database": "healthy",
    "tracker": "healthy",
    "websocket": "healthy"
  },
  "websocket": {
    "active_connections": 2
  },
  "circuit_breakers": {
    "feed_fetcher": {
      "state": "closed",
      "failure_count": 0
    }
  },
  "alerts": {
    "recent": [
      {
        "timestamp": "2025-11-21T10:30:00Z",
        "type": "feed_source_degraded",
        "severity": "warning",
        "message": "Feed availability below threshold"
      }
    ]
  },
  "database": {
    "detections_count": 125000,
    "tracks_count": 45000
  }
}

Core Endpoints

GET /api/feeds

Returns all feed metadata, online status, and vehicle detection status

Response:

{
  "feeds": [
    {
      "id": "CCTV-14-0620-009-002",
      "streamUrl": "https://...",
      "imageUrl": "https://.../snapshot",
      "roadName": "台62線",
      "locationMile": "9K+020",
      "lat": "25.10529",
      "lon": "121.7321",
      "direction": "W",
      "description": "..."
    }
  ],
  "status": {
    "CCTV-14-0620-009-002": true
  },
  "vehicleDetected": {
    "CCTV-14-0620-009-002": true
  },
  "lastUpdate": 1699999999.0
}

GET /api/feeds/{feed_id}/snapshot

Returns cached JPEG snapshot for a specific feed (with YOLO bounding boxes if vehicles detected)

GET /api/feeds/{feed_id}/stream

Returns live MJPEG stream (for future use)

GET /api/stats

Returns system statistics including detection performance

Response:

{
  "totalFeeds": 2402,
  "cachedFeeds": 2402,
  "workingFeeds": 1876,
  "offlineFeeds": 526,
  "vehiclesDetectedFeeds": 145,
  "lastUpdate": 1699999999.0,
  "cacheSize": 450.5,
  "detectionStats": {
    "processed": 1876,
    "skipped_unchanged": 430,
    "skipped_selective": 620
  },
  "websocket": {
    "connections": 2,
    "total_messages": 1234
  }
}

Search & Map Endpoints

GET /api/search

Search feeds with filters

Parameters:

  • road (optional): Road name filter
  • has_vehicles (optional): true/false
  • lat_min, lat_max, lon_min, lon_max (optional): Bounding box

Response:

{
  "query": {
    "road": "國道1號",
    "has_vehicles": true
  },
  "count": 42,
  "feeds": [ /* enriched feed objects with status and vehicleDetected */ ]
}

GET /api/map

Get all feeds as GeoJSON for map display

Response:

{
  "type": "FeatureCollection",
  "features": [
    {
      "type": "Feature",
      "geometry": {
        "type": "Point",
        "coordinates": [121.7321, 25.10529]
      },
      "properties": {
        "id": "CCTV-14-0620-009-002",
        "roadName": "台62線",
        "isWorking": true,
        "hasVehicles": false
      }
    }
  ]
}

Stream Out Endpoints

POST /api/stream/config

Configure Stream Out integration

Request:

{
  "enabled": true,
  "format": "cot",
  "ip": "127.0.0.1",
  "port": 8087,
  "latticeUrl": "",
  "latticeToken": "",
  "latticeSandboxToken": "",
  "latticeIntegration": "taiwan-cctv"
}

GET /api/stream/config

Get current Stream Out configuration

Database Management Endpoints

POST /api/database/reset

Reset database - clear all detections and tracks

Request: POST with empty body

Response:

{
  "status": "success",
  "message": "Database reset successfully",
  "deleted": {
    "detections": 125000,
    "tracks": 45000
  }
}

Note: This triggers a warning alert in the alert system

WebSocket Endpoint

WS /ws

Real-time updates for vehicle detections and feed status

Client → Server Messages:

{"action": "subscribe", "feed_id": "CCTV-14-0620-009-002"}
{"action": "unsubscribe", "feed_id": "CCTV-14-0620-009-002"}
{"action": "ping"}

Server → Client Messages:

// Vehicle detection
{
  "type": "detection",
  "feed_id": "CCTV-14-0620-009-002",
  "timestamp": "2025-11-12T21:30:45.123Z",
  "data": {
    "vehicle_count": 3,
    "vehicle_types": ["car", "car", "truck"],
    "tracked_vehicles": [...],
    "track_counts": {"car": 2, "truck": 1}
  }
}

// Feed status update
{
  "type": "feed_status",
  "feed_id": "CCTV-14-0620-009-002",
  "timestamp": "2025-11-12T21:30:45.123Z",
  "is_working": true,
  "has_vehicles": true
}

// Server heartbeat (every 30s)
{
  "type": "heartbeat",
  "timestamp": "2025-11-12T21:30:45.123Z",
  "connections": 2
}

// Stats broadcast
{
  "type": "stats",
  "timestamp": "2025-11-12T21:30:45.123Z",
  "data": { /* stats object */ }
}

Stream Out Integration

CoT (Cursor on Target)

Send vehicle detection events to TAK/ATAK systems via UDP.

Config: Format, IP, Port

Message: XML event with camera location, metadata, timestamp, video link

Example:

<event version="2.0" uid="TrafficCam-CCTV-14-0620-009-002" type="a-u-G" time="..." start="..." stale="...">
  <point lat="25.10529" lon="121.7321" hae="0" ce="50" le="0"/>
  <detail>
    <contact callsign="TrafficCam-CCTV-14-0620-009-002"/>
    <remarks>台62線 at 9K+020 - Vehicles detected</remarks>
    <link url="http://localhost:8001/api/feeds/CCTV-14-0620-009-002/snapshot"/>
  </detail>
</event>

Lattice (Anduril)

Publish vehicle track entities to Lattice platform.

Config: Format, URL, Environment Token, Sandbox Token (for sandboxes), Integration Name

Entity: Camera ID, location, VEHICLE platform type, 1-hour expiry

Note: Sandboxes need two tokens (Authorization + anduril-sandbox-authorization headers)

YOLO Vehicle Detection

  • Model: YOLOv8n (nano - fast inference)
  • Classes: Car, Bus, Truck (COCO dataset IDs: 2, 5, 7)
  • Confidence: 60% minimum
  • Min box size: 30px (filters false positives from distant objects)
  • Processing: ~50-100ms per image
  • Optimizations:
    • Skip detection on unchanged frames (hash-based)
    • Selective skip on empty feeds (every 2nd cycle)
    • Async processing via thread pool

Object Tracking

  • Tracker: Custom IoU-based tracker
  • Features:
    • Unique track IDs for each vehicle
    • Vehicle counting per class
    • Track persistence across frames
    • Confirmed tracks (minimum 3 hits)
  • Storage: Tracks saved to database with timestamps

Database Schema

Tables

feeds

  • id, road_name, location_mile, lat, lon, direction, last_online

detections

  • id, feed_id, timestamp, vehicle_count, detection_data (JSON)

vehicle_tracks

  • id, feed_id, track_id, first_seen, last_seen, vehicle_class, speed_estimate

Configuration

Edit config.yaml to customize:

detection:
  enabled: true
  confidence_threshold: 0.6
  min_box_size: 30
  vehicle_classes: [2, 5, 7]  # car, bus, truck

database:
  enabled: true
  path: "./data/detections.db"

tracking:
  enabled: true
  max_age: 30
  min_hits: 3
  iou_threshold: 0.3

websocket:
  enabled: true
  heartbeat_interval: 30

performance:
  batch_size: 240
  max_connections: 100
  selective_skip_interval: 2

Operational Monitoring

Health Checks

The system provides comprehensive health monitoring for all components:

  • Database: Connection health, query latency, connection count
  • YOLO Model: Model availability and readiness
  • HTTP Client: Feed fetcher operational status
  • WebSocket Manager: Connection manager health
  • Cache: Memory usage and size limits
  • Vehicle Tracker: Tracking system status
  • Feed Source: Feed availability and online percentage

Prometheus Metrics

Export metrics for monitoring with Prometheus/Grafana:

# Scrape metrics
curl http://localhost:8001/metrics

# Example Prometheus config
scrape_configs:
  - job_name: 'cctv-viewer'
    static_configs:
      - targets: ['localhost:8001']
    metrics_path: '/metrics'
    scrape_interval: 15s

Key metrics to monitor:

  • Feed availability: (cctv_feeds_online / cctv_feeds_total) * 100 → Target: >80%
  • Detection latency: histogram_quantile(0.95, cctv_yolo_inference_seconds_bucket) → Target: <200ms
  • Error rate: rate(cctv_errors_total[5m]) → Target: <5%
  • Cache size: cctv_cache_size_bytes / 1024^3 → Target: <1GB

Structured Logging

All logs are output in JSON format for easy parsing:

# View logs with jq
tail -f logs/cctv.log | jq

# Filter by level
tail -f logs/cctv.log | jq 'select(.level == "ERROR")'

# Watch specific component
tail -f logs/cctv.log | jq 'select(.context.component == "database")'

Circuit Breaker

Protects against cascading failures:

  • Monitors feed fetcher operations
  • Opens after 10 consecutive failures
  • 5-minute recovery timeout before retrying
  • States: closed (normal), open (disabled), half_open (testing)

Check state: curl http://localhost:8001/api/operational/status | jq '.circuit_breakers'

Alert Management

Tracks critical system events with 5-minute cooldown:

Alert types:

  • startup_failure - Component failed to start (critical)
  • database_init_failure - Database initialization error (critical)
  • high_error_rate - Error rate threshold exceeded (error)
  • feed_source_degraded - Too many feeds offline (warning)
  • cache_overflow - Cache size exceeds limits (warning)

View recent alerts: curl http://localhost:8001/api/operational/status | jq '.alerts.recent'

Monitoring Dashboard

Access the visual monitoring dashboard at http://localhost:8000/monitoring.html

Features:

  • Real-time component health visualization
  • System metrics (CPU, memory, disk)
  • Feed statistics with animated progress bars
  • Performance metrics
  • Database statistics
  • Recent alerts display
  • Database reset button
  • Auto-refresh every 5 seconds

Quick Health Check Commands

# Check overall system status
curl http://localhost:8001/health | jq '.status'

# Get unhealthy components
curl http://localhost:8001/health | jq '.components[] | select(.status != "healthy")'

# Check feed availability percentage
curl http://localhost:8001/api/operational/status | jq '.feeds.online_percentage'

# Watch system health (refreshes every 2 seconds)
watch -n 2 'curl -s http://localhost:8001/health | jq ".status, .system"'

Technical Details

  • Feeds: 2,402 Taiwan highway camera feeds
  • Backend: Python 3.8+, FastAPI, YOLOv8, SQLAlchemy, aiosqlite
  • Frontend: Vanilla JavaScript, Leaflet.js for maps
  • Memory: ~450 MB for cached JPEGs
  • Refresh Rate: 2 seconds (grid), 0.5 seconds (single feed)
  • Detection Rate: ~1876 feeds processed per cycle (~20-30 seconds)
  • Observability: Prometheus metrics, structured logging, health checks, circuit breakers

Troubleshooting

Backend won't start

# Install dependencies manually
python3 -m pip install -r requirements.txt

# Run with verbose logging
python3 backend/main.py

Feeds showing as offline

  • Check backend console for errors
  • Verify network connectivity to Taiwan
  • Some feeds may actually be offline (normal - typically ~500/2400)
  • Check /api/stats or /health endpoint for system health
  • Check feed availability: curl http://localhost:8001/api/operational/status | jq '.feeds'
  • View monitoring dashboard: http://localhost:8000/monitoring.html

YOLO not detecting vehicles

  • Check backend logs for "YOLO model loaded successfully"
  • Ensure PyTorch and dependencies installed correctly
  • Detection requires confidence ≥0.6 (60%) and minimum box size of 30 pixels
  • Only detects cars, buses, and trucks (motorcycles excluded)
  • Some cameras may not have vehicles in frame

WebSocket not connecting

  • Check browser console for WebSocket errors
  • Verify backend is running on port 8001
  • Check firewall/proxy settings
  • WebSocket URL: ws://localhost:8001/ws

Slow refresh rate

  • Backend logs show actual fetch time + YOLO processing time
  • Network latency to Taiwan servers affects speed
  • YOLO adds ~50-100ms per image
  • Adjust REFRESH_INTERVAL in frontend code if needed
  • Check detectionStats in /api/stats for performance metrics

High memory usage

  • Each cached JPEG is ~200 KB
  • 2,400 feeds = ~480 MB baseline
  • This is normal and expected
  • Database grows over time (detections history)
  • Monitor cache size: curl http://localhost:8001/api/operational/status | jq '.cache'
  • Check system memory: curl http://localhost:8001/health | jq '.system.memory'

Map markers not updating

  • Check browser console for "Map: WebSocket connected"
  • Markers update in real-time when vehicles detected
  • Try refreshing the page to reconnect WebSocket

Project Structure

videoviewer/
├── backend/
│   ├── main.py                 # FastAPI server + YOLO + WebSocket + monitoring
│   ├── database.py             # SQLAlchemy models and DB manager
│   ├── tracker.py              # Multi-object tracking
│   ├── websocket_manager.py    # WebSocket connection handler
│   └── observability.py        # Health checks, metrics, logging, alerts
├── client/
│   ├── index.html              # Grid view with vehicle detection
│   ├── map.html                # Interactive map view
│   ├── feed.html               # Single feed detailed view
│   └── monitoring.html         # Operational monitoring dashboard
├── data/
│   └── detections.db           # SQLite database (auto-created)
├── logs/
│   └── cctv.log                # Structured JSON logs (auto-created)
├── venv/                       # Python virtual environment
├── config.yaml                 # Configuration file
├── requirements.txt            # Python dependencies
├── start.sh                    # Startup script
├── IMPLEMENTATION_ROADMAP.md   # Feature roadmap
├── OPTIMIZATIONS.md            # Performance optimizations guide
└── README.md                   # This file

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