Tracking Evaluation Toolkit

The toolkit is used to evaluate visual trackers - it implements VOT measures accuracy and robustness on a baseline (reset-based) experiment. The toolkit is tested on Matlab and Octave and it is compatible with VOT sequences.

Note: The toolkit is developed for educational purposes only (course Advanced Computer Vision Methods at Faculty of Computer and information Science, University of Ljubljana) and it should not be used in research community.

How to create workspace:

1.) Checkout the repository in an empty folder - called toolkit
2.) Create new folder (not within toolkit) - called workspace
3.) Add the toolkit path to Matlab paths
4.) Run command (within workspace folder): create_tracker_workspace
5.) A script called workspace_config will appear in the workspace. Set the following variables according to your settings in this script:

c.tracker_name = ‘<tracker-name>’;
c.additional_paths = {‘add-paths’};  % Path to the source code directory
c.dataset_path = ‘path-to-dataset-directory’;

Design of a tracker:

Implement the tracker (with name <tracker-name>) in two functions:

tracker = <tracker-name>_initialize(img, bbox)
[tracker, bbox] = <tracker-name>_update(tracker, img)

You can see an example for the NCC tracker (located in examples/ncc folder).

Tracker evaluation:

In the workspace folder run command: run_experiment. The toolkit will run the tracker on all sequences and save results in the results folder. After the experiment is over you can call:

• performance_summary Tracking performance of the tracker specified in workspace_config will be printed out and a latex table will be generated in output folder.
  
• compare_trackers({‘list-comma-separated-tracker-names’}) Generates similar latex table as performance_summary script, but with multiple columns corresponding to multiple trackers. Additionally, an A-R plot will be generated and stored in output folder.