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contents.rst 2.61 KiB

Welcome to TRex's documentation!

|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

|grabs| 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 |grabs| 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.