Disclaimer: This project is now old and will no longer be updated. Please have a look at our new tracker CMT instead.

On this page you can find a C++ implementation of OpenTLD that was originally published in MATLAB by Zdenek Kalal. OpenTLD is used for tracking objects in video streams. What makes this algorithm outstanding is that it does not make use of any training data. This implementation is based solely on open source libraries, meaning that you do not need any commercial products to compile or run it.

The easiest way to get started is to download the precompiled binaries that are available for Windows and Ubuntu. If you have a webcam attached to your PC, you can simply execute tld (on Ubuntu) or tld.exe (on Windows) in order to try it out. For other configuration options, please have a look at the README file. There is also a discussion group of the TLD community where you might get some information. A documentation of the internals as well as other possibly helpful information is contained in this master thesis.

The source code of OpenTLD is published under the terms of the GNU General Public License, so feel free to dig through it. Please understand that this software is meant as a demonstration of what state-of-the-art computer vision algorithms are currently capable of and not as a ready-to-use product. If you find errors in the program, please report them at the GitHub issues page.

Result Videos


You can download this project in either zip or tar formats. You can also browse the source code on GitHub or clone the project directly with Git by running:

$ git clone git://

Frequently Asked Questions

Can I use OpenTLD for multi-target tracking?

In principle OpenTLD is meant to track single objects only, but there is nothing that stops you from employing multiple instances of OpenTLD for multi-target tracking. It is rather difficult to implement multi-target tracking in OpenTLD itself, as the sliding-window approach is heavily optimised for the specific dimensions of the object. If your objects are all of the same size, it might be worthwile investigating this option.