A comparison of the previous state-of-the-art WOFT with the proposed WOFTSAM.
Abstract
Recent state-of-the-art visual trackers produce high quality and long-term-stable segmentation masks. We propose to leverage these strengths for planar object tracking, in which the goal is to estimate a precise 8-degrees-of-freedom homography pose, a geometric representation not estimated by segmentation trackers. We present SAM-H - a planar object tracker that estimates homographies from segmentation mask contours via a training-free pipeline. When SAM-H is applied to masks from SAM 2, it sets a new state-of-the-art performance on the challenging PlanarTrack benchmark by a large margin, +18.4pp on the p@5 metric. We further show that segmentation-based and correspondence-based homography estimation are complementary, and propose WOFTSAM, which out-performs all prior methods on both PlanarTrack and POT-210. We also provide precise re-annotations of PlanarTrack initial poses, enabling more accurate benchmarking in the high-precision p@5 metric.
PlanarTrack Initial Frame Re-Annotation
We have carefully manually re-annotated the initial frames of the PlanarTrack TST dataset as described in the paper. We provide the re-annotation here.
Bibtex
Please cite our paper in case you use its source code, results, or the re-annotation.
@article{serych2026woftsam,
title={Segmentation-Guided Homography Estimation for Long-Term Planar Tracking},
author={Serych, Jonas and Matas, Jiri},
journal={arXiv preprint arXiv:2602.19624},
year={2026}
}
Acknowledgments
This work was supported by the National Recovery Plan project CEDMO 2.0 NPO (MPO 60273/24/21300/21000), the EC Digital Europe Programme project CEDMO 2.0 no. 101158609, and by the Research Center for Informatics project CZ.02.1.01/0.0/0.0/16_019/0000765 funded by OP VVV.