Dog Daycare Monitor

A computer vision project to monitor dog daycare locations by collecting and analyzing camera feed screenshots at regular intervals.

Overview

This project automatically captures screenshots from dog daycare webcam feeds and processes them to detect and track dogs and humans. The system is currently deployed on a Raspberry Pi 5 with a 10-minute cron job for continuous monitoring.

Features

Current Functionality

• Automated Screenshot Collection (capture/): Downloads images at regular intervals from multiple dog daycare camera feeds
• Object Detection (process/): Uses YOLOv8 model to detect and draw bounding boxes around:
  • Dogs
  • Humans
  • Displays confidence scores for each detection

Future Goals

• Identify individual dogs (your specific dog)
• Detect dog stances and behaviors
• Track the ratio of dogs to people over time
• Generate analytics and alerts based on activity patterns

Project Structure

dog-daycare/
├── capture/
│   └── download-data.py    # Script to download screenshots from camera feeds
├── process/
│   └── process-image.py    # Script to detect dogs and humans in images
└── data/                   # Storage for captured images (gitignored)

Usage

Capturing Screenshots

The capture script downloads images from configured camera feeds:

python capture/download-data.py

Images are saved to data/<camera_name>/<timestamp>.jpg

Processing Images

Analyze an image to detect dogs and humans:

python process/process-image.py <path_to_image>

This will display the image with bounding boxes around detected dogs and people.

Deployment

Currently running on a Raspberry Pi 5 with a cron job that executes every 10 minutes to collect screenshots automatically.

Technologies

• Python: Core programming language
• YOLOv8: State-of-the-art object detection model (via Ultralytics)
• OpenCV: Image processing and visualization
• Requests: HTTP client for downloading images

Requirements

• Python 3.x
• ultralytics
• opencv-python
• requests