Accurate Planar Tracking With Robust Re-Detection
This work addresses planar tracking for computer vision applications, offering incremental improvements over existing methods.
The authors tackled planar tracking by introducing SAM-H and WOFTSAM, which combine segmentation tracking with homography estimation and re-detection, resulting in state-of-the-art performance with improvements of +12.4 and +15.2 percentage points on the p@15 metric.
We present SAM-H and WOFTSAM, novel planar trackers that combine robust long-term segmentation tracking provided by SAM 2 with 8 degrees-of-freedom homography pose estimation. SAM-H estimates homographies from segmentation mask contours and is thus highly robust to target appearance changes. WOFTSAM significantly improves the current state-of-the-art planar tracker WOFT by exploiting lost target re-detection provided by SAM-H. The proposed methods are evaluated on POT-210 and PlanarTrack tracking benchmarks, setting the new state-of-the-art performance on both. On the latter, they outperform the second best by a large margin, +12.4 and +15.2pp on the p@15 metric. We also present improved ground-truth annotations of initial PlanarTrack poses, enabling more accurate benchmarking in the high-precision p@5 metric. The code and the re-annotations are available at https://github.com/serycjon/WOFTSAM