🎯 Object Tracking

An experimental Object Tracking with Python and OpenCV.
This repository contains scripts and datasets to explore tracking objects in video streams and images, helping me understand computer vision concepts step by step.

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📂 Dataset Source
The dataset used in this project is from:

• Rubik’s Cube Dataset

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✨ Technologies

• Python
• OpenCV
• NumPy
• Matplotlib (for visualization)

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🚀 Features

• Track objects in video or image sequences
• Experiment with different tracking algorithms
• Visualize bounding boxes and tracking paths
• Work with sample datasets in rubik_dataset/

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📂 Repository Contents

• main.py – Main Python script for running object tracking experiments
• rubik_dataset/ – Sample dataset for testing tracking algorithms
• requirements.txt – Python dependencies
• README.md – This documentation

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📍 The Process

I started this repository to learn and experiment with Object Tracking using OpenCV.
Most tutorials online felt either too abstract or too incomplete, so I wanted a hands-on playground.

I began by loading video or image sequences and applying tracking algorithms. Then I visualized the tracking results to see how well objects were detected and followed. Each experiment in main.py represents a step in learning how different algorithms perform under various conditions.

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🚦 Running the Project

1. Clone the repository:

   git clone https://github.com/tegar17/object-tracking-cv2.git
   cd object-tracking-cv2

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🖥️ Preview

preview.output.mp4