Animal Detection and Tracking System

Overview

This project presents a comprehensive system for detecting, analyzing, and tracking animals. It integrates cutting-edge techniques and models to provide accurate results across three main functionalities:

1. Animal Detection from Images and Videos

• Techniques Used:
  • Haar Feature-based Cascade Classifier
  • YOLO (You Only Look Once)
• Description: This module detects animals in images and videos. Haar features facilitate structured pattern recognition, while YOLO enables real-time and efficient object detection.

2. Animal Mood Detection from Audio

• Techniques Used:
  • Custom-trained machine learning model
  • Dataset provided ("animal_mood_dataset")
• Model Outputs:
• Description: This component analyzes animal vocalizations to infer emotional states (e.g., happy, stressed) using a machine learning model trained on the provided dataset.

3. Animal Movement Tracking

• Techniques Used:
  • Breadth-First Search (BFS)
  • Depth-First Search (DFS)
• Description: This feature tracks animal movements within a predefined area using BFS and DFS algorithms, offering systematic exploration and navigation.

Instructions

1. Setup Environment:

   • Ensure compatibility with the specified Python version and libraries.

2. Data Preparation:

   • Store image and video files for detection in the designated directory.
   • Save audio files for mood detection in the required format.
   • Use the provided "animal_mood_dataset" for training or fine-tuning the mood detection model if necessary.

3. Running the Modules:

   • Animal Detection: Execute the respective script for Haar or YOLO-based detection.
   • Animal Mood Detection: Load the custom-trained model and run the script to analyze audio inputs.
   • Animal Movement Tracking: Implement BFS/DFS algorithms to track and visualize animal positions.

Project Structure

• animal_mood_dataset/: Dataset for training and testing the mood detection model.
• AI_ES_CCP.ipynb: Jupyter Notebook containing scripts, models, and visualizations.

Dependencies

• Python 3.x
• OpenCV
• TensorFlow or PyTorch
• NumPy
• Matplotlib

Notes

• Ensure proper placement of all data files to avoid runtime errors.
• Consult the comments in the Jupyter Notebook for in-depth explanations of the code.

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Developed by Abdul Basit