Wildlife Tracking Project

Use camera trap images and machine learning to monitor endangered species

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Project Overview

This project uses camera trap data from wildlife conservation areas to train machine learning models that automatically identify and track animal species. By analyzing images captured in remote habitats, we help conservationists understand animal behavior and population patterns.

How It Works

  • Cameras take photos in wildlife areas
  • Images are uploaded to our analysis system
  • ML models identify species and track movements
  • Generate conservation reports for biologists

Technology Stack

  • Python for image processing
  • PyTorch for model training
  • TensorFlow Lite for edge devices
  • PostgreSQL for data storage

Getting Started

# Sample image processing code
from PIL import Image

def process_image(image_path):
    with Image.open(image_path) as img:
        # Resize and convert for ML model
        processed = img.resize((224, 224))
        return np.array(processed)

To participate, you'll need basic Python knowledge and access to the camera trap dataset. We'll provide detailed setup instructions in our GitHub repository. You'll also work with Google Colab notebooks for training and analysis.

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