trackR - Multi-object tracking with R

Description

trackR is an object tracker for R based on OpenCV. It provides an easy-to-use (or so we think) graphical interface allowing users to perform multi-object video tracking in a range of conditions while maintaining individual identities.

trackR uses RGB-channel-specific background subtraction to segment objects in a video. A background image can be provided by the user or can be computed by trackR automatically in most situations. Overlapping objects are then separated using cross-entropy clustering, an automated classification method that provides good computing performance while being able to handle various types of object shapes (see the CEC package for R for more information on cross-entropy clustering). Most of the tracking parameters can be automatically estimated by trackR or can be set manually by the user.

trackR also allows users to exclude parts of the image by using masks that can be easily created and customized directly within the app.

Finally, trackR provides several convenience apps to correct common errors that occurs during video recording, to manually inspect and fix tracking errors, and to export publication-ready videos showing the moving objects with their track overlaid on top of them.

Quick start guides

FAQ

How does trackR compare to other video tracking solutions? Did we really need another one?

trackR belongs to the category of the 'classical' tracking programs. It relies on good ol' fashion image processing, robust cross-entropy clustering, and simple, yet efficient, assignment algorithms (the Hungarian method in this case). trackR does not include (for now) any fancy machine learning methods like those that can be found in the fantastic idtracker.ai for instance. The downside is that trackR's tracking reliability is inferior to the more advanced software (in particular when the objects cross paths); the upside is that it is fast and does not require a beast of a computer to run.

trackR is more similar in spirit to tracking software such as Ctrax, tracktor, and the sadly defunct SwisTrack. It will most likely provide tracking reliability equivalent to these excellent programs. However, we believe that trackR's object segmentation and separation algorithms are generally more robust and should therefore be capable of handling a wider variety of situations.

Will something break? Can I use trackR in 'production' mode?

Something will definitely break. This is version 0.5 of the software, there is still a long way to go before it is a fully finished program. This being said, it will work fine in most cases and is certainly usable for most tracking projects. If you run into an issue, please report it at: https://github.com/swarm-lab/trackR/issues.

What features are in the works for future versions of trackR?

At the moment, I am considering including the following features in future iterations of trackR:

~~Add the possibility to save and restore settings for future use.~~ [DONE]
~~Add the possibility to indicate whether the objects to track are darker or lighter than the background.~~ [DONE]
Add live tracking using camera feed. [LONG TERM GOAL]
Add the possibility to feed the tracking data through a custom R script. Currently, the data are saved in a CSV file and the user has to re-import them into R for post-processing. While this is fine for most use cases, it would not work if the user needs some form of live processing of the tracking data. [LONG TERM GOAL]
~~Add some sort of GUI for manually correcting tracks.~~ [DONE]
Add AI-powered track correction. [IN PROGRESS]
Add app to automatically compute classic statistics on trajectories for users that do not want to/cannot run their own data analysis pipeline. [LONG TERM GOAL]
Add possibility to include timestamps, title, subtitle, etc., in videos exported by trackPlayer. [IN PROGRESS]

How can I help?

trackR is an open-source project, meaning that you can freely modify its code and implement new functionalities. If you have coding skills, you are more than welcome to contribute new code or code improvement by submitting pull requests on the GitHub repository of the project at: https://github.com/swarm-lab/trackR. I will do my best to review and integrate them quickly.

If you do not feel like contributing code, you can also help by submitting bug reports and feature requests using the issue tracker on the GitHub repository of the project at: https://github.com/swarm-lab/trackR/issues. These are extremely helpful to catch and correct errors in the code, and to guide the development of trackR by integrating functionalities requested by the community.