A Long-Term Discriminative Single Shot Segmentation Tracker

International Electrotechnical and Computer Science Conference (ERK), 2022
State-of-the-art long-term visual object tracking methods are limited to predict target position as an axis-aligned bounding box. Segmentation-based trackers exist, however they do not address long-term disappearances of the target. We propose a long-term discriminative single shot segmentation tracker -- D3SLT, which addresses the above shortcomings. The previously developed short-term D3S tracker is upgraded with a global re-detection module, based on an image-wide discriminative correlation filter response and Gaussian motion model. An online learned confidence estimation module is employed for robust estimation target disappearance. Additional backtracking module enables recovery from tracking failures and further improves tracking performance. D3SLT performs close to the state-of-the-art long-term trackers on the bou\-nding box based VOT-LT2021 Challenge, achieving F-score of 0.667, while additionally outputting segmentation masks.


<a href="http://prints.vicos.si/publications/412">A Long-Term Discriminative Single Shot Segmentation Tracker</a>