Description
trackRcv is an object tracker for R based on Python OpenCV. It provides easy-to-use (or so I think) apps for performing multi-object video tracking in a range of conditions while maintaining individual identities. trackRcv also provides convenience apps to correct common errors that occurs during tracking, and to export publication-ready videos showing the moving objects with their track overlaid on top of them.
trackRcv uses traditional computer vision techniques to segment and track objects in a video. Object separation is done 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). For more advanced segmentation and tracking based on deep learning approaches, you can try my YOLO11-based trackRai package instead (a powerful CUDA-enabled NVIDIA graphics is recommended in that case).
Installation
You can install the development version of trackRcv from GitHub with:
# install.packages("pak")
pak::pak("swarm-lab/trackRcv")FAQ
How does trackRcv compare to other video tracking solutions? Did we really need another one?
trackRcv belongs to the category of the ‘classical’ tracking programs, similar in spirit to tracking software such as Ctrax, tracktor, and the sadly defunct SwisTrack. It relies on good ol’ fashion image processing, robust cross-entropy clustering, and simple, yet efficient, assignment algorithms (the Hungarian method in this case). trackRcv does not use any fancy machine learning methods. The downside is that trackRcv’s tracking reliability may be inferior to more advanced software; the upside is that it does not require a beast of a computer to run.
If trackRcv is not capable of tracking objects in your videos with the accuracy that you require, you can try its YOLO-based AI counterpart, trackRai
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Will something break? Can I use trackRcv in ‘production’ mode?
Something will definitely break. This is mostly a one-person operation and I cannot promise that I have fully tested every single scenario that could challenge trackRcv. 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/trackRcv/issues.
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How can I help?
trackRcv 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/trackRcv. 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/trackRcv/issues. These are extremely helpful to catch and correct errors in the code, and to guide the development of trackRcv by integrating functionalities requested by the community.