pyTAG: python-based interactive training data generation for visual tracking algorithms (Conference Presentation)

In this study, a rapid training data and ground truth generation tool has been implemented for visual tracking. The proposed tool's plugin structure allows integration, testing, and validation of different trackers. The tracker can be paused, resumed, forwarded, rewound and re-initialized on the run, after it loses the object, which is a needed step in the training data generation. This tool has been implemented to assist researchers to rapidly generate ground truth and training data, fix annotations, run and visualize their own single object trackers, or existing object tracking techniques.