Welcome to TRex’s documentation!

Note

This page (and the github repository at https://github.com/mooch443/trex) will be updated frequently at the moment, since TRex is still in active development. If you find any issues, please report them using the github issue tracker!

TRex is a tracking software designed to track and identify individuals and other moving entities using computer vision and machine learning. The work-load is split into two (not entirely separate) tools:

  • TGrabs: Record or convert existing videos, perform live-tracking and closed-loop experiments

  • TRex: Track converted videos (in PV format), use the automatic visual recognition, explore the data with visual helpers, export task-specific data, and adapt tracking parameters to specific use-cases

Workflow

TGrabs always has to be used first. TRex is optional in some cases. Use-cases where TRex is not required include:

  • Just give me tracks: The user has a video and wants positional, or posture-related data for the individuals seen in the video. Maintaining identities is not required.

  • Closed-loop: React to the behavior of individuals during a trial, e.g. lighting an LED when individuals get close to it, or run a python script every time individual 2 sees individual 3.

Whereas other use-cases are:

  • Maintaining identities: Individuals are required to be assigned consistent identities throughout the entire video. Any results involving automatic identity correction will have to use TRex.

  • Adjusting parameters with visual feedback: While TGrabs includes a lot of the functionality of TRex, it currently has no interface to directly test out parameters. Tracking parameters, specifically, have to be tested in TRex. This is useful, e.g. when trying to figure out parameters for a batch process or adapting parameters for specific purposes.

  • Exploring and generating videos for presentations: TRex provides a rich set of functionalities for generating heatmaps and other useful visual information, as well as offering support to record anything that is on-screen to a AVI video file. For example, one can follow a subset of individuals and record every frame (lag-free and making sure that no frames are skipped). Any changes to the interface will be visible in the video as well.