Efficient tracking of ants in long video with GPU and interaction

Behavior analysis of social insects requires robust tracking over many frames. Automated tracking methods are not reliable for tracking over long video. And they are prone to a quick accumulation of error from one mis-tracking. However, searching and correcting of mis-tracking is time-consuming. In this paper, we present an efficient method for achieving robust tracking of multiple ants over a long video. First, our method minimizes the user wait time by speeding up tracking with a GPU. Second, it minimizes the amount of data the user needs to validate by automatically searching for potential errors and presenting them for user validation and correction. User studies with three participants on a 10,000 frame video demonstrates that (1) the speed of tracking is 16x faster with GPU optimization, (2) tracking accuracy was 96%, which is a 25% improvement over no user interaction, (3) users examined less than 0.6% of frames for validation and correction.