VideoAnnotation : Différence entre versions
De Transport
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merging, splitting, moving positions and adding missing positions (extend trajectories) | 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 | * 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/ | ||
Version actuelle en date du 30 juin 2021 à 16:08
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/
