BUILD STATUS

Contact Information

If you have any questions or need support, feel free to reach out to our team members:

• Şevval Belkıs Dikkaya: Head of the team, working on software, electronics, and mechanics. LinkedIn
• Muhammed Sezer: Responsible for electronics and software, and the maintainer of this package. LinkedIn
• Metehan İçöz: Responsible for mechanics. LinkedIn
• Zeynep Keleş: Working on software, electronics, and mechanics. LinkedIn

Feel free to connect with us on LinkedIn and mention any specific questions or issues related to the project. We’ll be happy to help!

Object Detection and Tracking ROS2 Package

Table of Contents

1. Introduction
2. Dependencies
3. Installation
4. Usage
5. Node Descriptions
   • object_detector
     • Published Topics
     • Subscribed Topics
   • object_tracker
     • Published Topics
     • Subscribed Topics
   • best_object_selector
     • Published Topics
     • Subscribed Topics
   • vehicle_status
     • Published Topics
     • Subscribed Topics
6. Parameters
7. Messages
8. Code Examples
9. Troubleshooting
10. Contributing
11. License
12. Hardware Used
13. Resources

Introduction

The introduction section should provide a brief overview of the main features, capabilities, and use cases of the package. Explain the purpose of the package and what it aims to achieve.

Eflatun is a package that contains a comprehensive set of tools and algorithms for implementing and testing machine learning models. This project is aimed at providing developers with a modular and extensible software framework for developing autonomous systems, such as robots, drones, and other intelligent machines. The package includes nodes that track and detect an object and select the best out of them. It also includes instructions for how to install and use the framework, as well as examples of how to develop various autonomous systems using the eflatun_src framework..

The package is capable of data preprocessing, feature engineering, and model evaluation. It also supports parallel computing, which enables users to take advantage of multi-core CPUs for faster processing of large datasets.

The package can be used for a variety of domains such as finance, healthcare, marketing, and research purposes. Its features and capabilities make it a valuable tool for anyone interested in machine learning and data science. The use cases of Eflatun include predictive modeling, anomaly detection, image and speech recognition, natural language processing (NLP), and recommender systems.

Dependencies

In the Dependencies section, list and explain all external dependencies required for the package to work, including specific versions if necessary. Add instructions for installing these dependencies.

Installation

The Installation section should provide step-by-step installation instructions for the package. Include information on installing the package from source or using a package manager, if applicable.

Usage

The Usage section should include detailed examples demonstrating how to use the package. Provide code snippets, terminal commands, or configuration files to help users understand the package’s functionality better.

Node Descriptions

🔎 Object Detector Node

The object_detector node is responsible for detecting objects in a video stream. It processes the video frames and identifies objects based on the provided model. The node does not subscribe to any topics.

Published Topics

Subscribed Topics

The object_detector node does not subscribe to any topics.

📌 Object Tracker Node

The object_tracker node is responsible for tracking detected objects in a video stream. It subscribes to the /webcam/detections topic, receives detected objects, and updates their tracking information. The tracking information is then published to the /tracker/tracked_objects topic.

Published Topics

Subscribed Topics

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