Webcam Motion Monitor

Webcam motion detection and monitoring system for WSL2 with USB/IP passthrough. Use when setting up motion detection on a USB webcam, monitoring camera snapshots, auto-analyzing images with AI, or managing webcam-based security/activity monitoring. Supports Insta360 Link and other UVC cameras via usbipd on Windows.

Webcam Monitor

Complete webcam motion detection and monitoring system for WSL2 environments.

Overview

This skill provides:

• Motion detection with automatic snapshot capture
• Real-time folder monitoring with auto-analysis queue
• Web-based live preview (port 8081)
• Auto-cleanup of old snapshots (1 hour default)
• Person identification via AI image analysis

Prerequisites

Windows Side (PowerShell as Admin):

winget install usbipd
usbipd list
usbipd bind --busid=1-4
usbipd attach --wsl --busid=1-4

WSL2 Side:

ls /dev/video*  # Should show /dev/video0

Note: If /dev/video* does not appear, you may need to restart WSL2:

# In Windows PowerShell:
wsl --shutdown
# Then re-attach the camera:
usbipd attach --wsl --busid=1-4

Quick Start

1. Start Motion Detection

python3 scripts/motion_detector_headless.py

• Runs in background (no GUI)
• Saves snapshots to ~/.openclaw/workspace/camera/snapshots/
• Logs to ~/.openclaw/workspace/camera/motion.log

2. Start Folder Monitor with Auto-Cleanup

python3 scripts/watcher_with_cleanup.py

• Monitors for new snapshots every 2 seconds
• Auto-deletes snapshots older than 1 hour (every 5 minutes)
• Queues new images for analysis
• Logs to ~/.openclaw/workspace/camera/watcher.log

3. Start Web Preview (Optional)

python3 scripts/web_preview.py

• Opens web server on port 8081
• View at http://localhost:8081
• Note: Stop motion detection first — camera can only be used by one program

Configuration

Edit scripts/motion_detector_headless.py:

• MOTION_THRESHOLD — Sensitivity (lower = more sensitive, default: 25)
• MIN_CONTOUR_AREA — Minimum motion area (default: 500)
• SNAPSHOT_COOLDOWN — Seconds between snapshots (default: 5)
• MAX_AGE_HOURS — Auto-cleanup threshold (default: 1)

Scripts

Script	Purpose
motion_detector_headless.py	Background motion detection daemon
watcher_with_cleanup.py	Folder monitor + auto-cleanup + analysis queue
web_preview.py	Web-based live preview (port 8081)
snapshot.sh	Quick manual snapshot
cleanup.py	Manual cleanup of old snapshots

File Locations

• Snapshots: ~/.openclaw/workspace/camera/snapshots/
• Logs: ~/.openclaw/workspace/camera/motion.log
• Watcher Log: ~/.openclaw/workspace/camera/watcher.log
• Analysis Queue: ~/.openclaw/workspace/camera/analysis_queue/

Troubleshooting

"Could not open camera"

# Check USB passthrough
ls /dev/video*

If no video devices appear, restart WSL2:

# In Windows PowerShell:
wsl --shutdown
# Then re-attach the camera:
usbipd attach --wsl --busid=1-4

Then verify in WSL2:

ls /dev/video*  # Should now show /dev/video0

Permission denied

sudo chmod 666 /dev/video0

Camera in use

Only one program can use the camera at a time:

# Stop motion detection
pkill -f motion_detector

# Stop web preview
pkill -f web_preview

# Stop watcher
pkill -f watcher

Person Identification

The watcher can queue snapshots for AI analysis. To identify people:

1. Update MEMORY.md with person details:

### Webcam Identification
- **Person:** [Name]
- **Appearance:** [Description - include hair, jewelry, clothing, wigs if applicable]
- **Setting:** [Location details]

Example:

### Webcam Identification
- **Person:** Jade
- **Appearance:** Middle-aged, light-colored/graying hair (short, receding), often wears star-shaped pendant necklace, sometimes in bathrobe/robe when at desk, sometimes wears shoulder-length hair wig
- **Setting:** Home office with black mesh chair, cat tree, bookshelves

2. Analyze queued images using the image tool with qwen model

Auto-Cleanup

By default, snapshots older than 1 hour are automatically deleted every 5 minutes.

To change:

• Edit CLEANUP_INTERVAL in watcher_with_cleanup.py (seconds)
• Edit MAX_AGE_HOURS in watcher_with_cleanup.py (hours)

Integration with OpenClaw

The watcher creates queue files in analysis_queue/ that can trigger automatic image analysis by the main agent. Check for new queue files and analyze with:

# Example: Check queue and analyze
queue_dir = Path.home() / ".openclaw/workspace/camera/analysis_queue"
for queue_file in queue_dir.glob("analyze_*.txt"):
    image_path = queue_file.read_text().strip()
    # Analyze image with image tool