Perception And Computer Vision In MATLAB

Abstract

1. Introduction

This repository includes codes created for a graduate level Perception course at the University of Maryland. The repo includes codes for AR tag detection, colored underwater buoy detection, lane detection, car detection , traffic sign recognition and visual odometry for a moving car

The three colored buoys were found and tracked through a video using 1-D Gaussian , 3-D Gaussian and Gaussian Mixture Modeling for comparative studies

The output detects an AR Tag , identifies the tag based on the tag encoding and calculates the camera poses to superimpose a cube on the tag

2. Objective

This repository includes codes created for a graduate level Perception course at the University of Maryland. The repo includes codes for AR tag detection, colored underwater buoy detection, lane detection, car detection , traffic sign recognition and visual odometry for a moving car

This project demonstrates how MATLAB can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• See the project README for exact requirements
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/sudrag/Perception-and-Computer-Vision-in-MATLAB.git
cd Perception-and-Computer-Vision-in-MATLAB

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by sudrag and published on GitHub under the GNU General Public License v3.0. Please follow the license terms and credit the original author when you use or modify this code.