VideoAnnotation

The goal of this project is to develop a graphical tool to annotate video data, in particular road user trajectories and their characteristics (user type). It will help correct and improve the output of the Traffic Intelligence feature-based tracker.

merging, splitting, moving positions and adding missing positions (extend trajectories)

Bitbucket repository: https://bitbucket.org/Wendlasida/trafficintelligenceannotationtool/

Tests

• a test of manual tracking on at least 5 min or 50 vehicles (without starting from the database produced by the automated tracker). The test should be done once with a homography, and a shorter one (on a few tracks (~5) without a homography.
• a test of correcting the automated tracker on a longer period (eg 30 min video, 100s of road users)

In addition to testing the software, this will give us estimates of the time required to clean the data.

• If the previous tests contain few pedestrians (<10), you should extend both so that you track at least 20 pedestrians.

These tests must include saving the data to SQLite, then reloading it and replaying it with the display-trajectories script and plotting them in ipython.

Resources

• Traffic Intelligence project
• Existing tools
  • https://github.com/openvinotoolkit/cvat
  • http://www.cs.columbia.edu/~vondrick/vatic/
  • tool from OpenCV (not free): https://superannotate.com/download
  • example of static image annotation http://www.robots.ox.ac.uk/~vgg/software/via/