https://github.com/abdulbasit110/animal-mood-and-movement-detection

A project for animal detection using Haar Cascade and YOLO, mood analysis from audio with a custom model, and movement tracking via BFS and DFS. Includes datasets, scripts, and models for implementation.
https://github.com/abdulbasit110/animal-mood-and-movement-detection

animal-detection machine-learning model-training mood-detection movement-detection python

Last synced: 23 days ago
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A project for animal detection using Haar Cascade and YOLO, mood analysis from audio with a custom model, and movement tracking via BFS and DFS. Includes datasets, scripts, and models for implementation.

• Host: GitHub
• URL: https://github.com/abdulbasit110/animal-mood-and-movement-detection
• Owner: Abdulbasit110
• Created: 2024-12-25T18:02:47.000Z (about 1 month ago)
• Default Branch: master
• Last Pushed: 2024-12-25T18:17:51.000Z (about 1 month ago)
• Last Synced: 2024-12-25T19:20:55.589Z (about 1 month ago)
• Topics: animal-detection, machine-learning, model-training, mood-detection, movement-detection, python
• Language: Jupyter Notebook
• Homepage:
• Size: 50.5 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: readme.md

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README

        
# 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:**

     ![alt text]()

   - **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**