Home Projects hdl_people_tracking
hdl_people_tracking
C++

hdl_people_tracking

Real-time people tracking using a 3D LIDAR

by koide3 · GitHub
Stars
Forks
Created
Last commit
Language
human-detectionlidarperson-trackingBSD-2-ClauseC++
View on GitHub
In plain words

Track people in real-time using a 3D LIDAR sensor with advanced detection techniques.

You maintain this project?

Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.

Claim this page →
hdl_people_tracking — GitHub preview card
📈 Star history
325324
2026-07-202026-08-31
📈 Track hdl_people_tracking

Get an email alert on its next release or when it starts trending — never miss the moment.

Free · no card · unsubscribe anytime
Get email alerts →
📄 About

Real-time people tracking using a 3D LIDAR

hdl_people_tracking has 324 stars on GitHub. It has been forked 100 times. hdl_people_tracking is written mainly in C++. It has been in active development since 2018. hdl_people_tracking is available under the BSD-2-Clause license. Its main topics are human-detection, lidar, person-tracking, ros.

Frequently asked questions

What is hdl_people_tracking?

Real-time people tracking using a 3D LIDAR

Is hdl_people_tracking open source?

hdl_people_tracking is an open-source project. It is released under the BSD-2-Clause license.

Is hdl_people_tracking free?

Yes. hdl_people_tracking is free and open source — you can use, modify and self-host it.

What license does hdl_people_tracking use?

hdl_people_tracking is available under the BSD-2-Clause license.

What language is hdl_people_tracking written in?

hdl_people_tracking is written mainly in C++.

🏅 Maintainer of this project?
olud.ai badge — hdl_people_tracking

Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.

[![olud.ai](https://olud.ai/badge.php?tool=koide3-hdl-people-tracking)](https://olud.ai/project/koide3-hdl-people-tracking.html)
More badge options →
🧬 Shares DNA with🧬 View the DNA map →

Measured from GitHub topics shared by both projects, weighted by how rare each topic is.