Accurate Planar Tracking With Robust Re-Detection

• URL: http://arxiv.org/abs/2602.19624v1
• Date: Mon, 23 Feb 2026 09:13:55 GMT
• Title: Accurate Planar Tracking With Robust Re-Detection
• Authors: Jonas Serych, Jiri Matas,
• Abstract summary: 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.<n>The proposed methods are evaluated on POT-210 and PlanarTrack tracking benchmarks, setting the new state-of-the-art performance on both.<n>We also present improved ground-truth annotations of initial PlanarTrack poses, enabling more accurate benchmarking in the high-precision p@5 metric.
• Score: 17.216623635232928
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

Related papers

• SAM-Aug: Leveraging SAM Priors for Few-Shot Parcel Segmentation in Satellite Time Series [3.4368348203064283]
  We propose SAM-Aug, a new annotation-efficient framework to improve few-shot land cover mapping.<n>Our approach constructs cloud-free composite images from temporal sequences and applies SAM in a fully unsupervised manner.<n>Experiments on the PASTIS-R benchmark under a 5 percent labeled setting demonstrate the effectiveness and robustness of SAM-Aug.
  arXiv  Detail & Related papers  (2026-01-14T03:18:04Z)
• Benchmarking SAM2-based Trackers on FMOX [1.5866079116942815]
  We propose to benchmark high performing trackers (SAM2, EfficientTAM, DAM4SAM and SAMURAI) on datasets containing fast moving objects (FMO)<n>Overall the trackers DAM4SAM and SAMURAI perform well on more challenging sequences.
  arXiv  Detail & Related papers  (2025-12-10T13:21:09Z)
• Distractor-Aware Memory-Based Visual Object Tracking [17.945503249662675]
  We propose a distractor-aware drop-in memory module and introspection-based management method for SAM2.<n>Our design effectively reduces the tracking drift toward distractors and improves redetection capability after object occlusion.<n>We show DAM4SAM outperforms SAM2.1 on thirteen benchmarks and sets new state-of-the-art results on ten.
  arXiv  Detail & Related papers  (2025-09-17T09:54:27Z)
• Seg2Track-SAM2: SAM2-based Multi-object Tracking and Segmentation for Zero-shot Generalization [3.108551551357326]
  Seg2Track-SAM2 is a framework that integrates pre-trained object detectors with SAM2 and a novel Seg2Track module.<n>Seg2Track-SAM2 achieves state-of-the-art (SOTA) performance, ranking fourth overall in both car and pedestrian classes on KITTI MOTS.<n>Results confirm that Seg2Track-SAM2 advances MOTS by combining robust zero-shot tracking, enhanced identity preservation, and efficient memory utilization.
  arXiv  Detail & Related papers  (2025-09-15T10:52:27Z)
• SAMITE: Position Prompted SAM2 with Calibrated Memory for Visual Object Tracking [58.35852822355312]
  Visual Object Tracking (VOT) is widely used in applications like autonomous driving to continuously track targets in videos.<n>To address these issues, some methods propose to adapt the video foundation model SAM2 for VOT, where the tracking results of each frame would be encoded as memory for conditioning the rest of frames in an autoregressive manner.<n>We present a SAMITE model, built upon SAM2 with additional modules, to tackle these challenges.
  arXiv  Detail & Related papers  (2025-07-29T12:11:56Z)
• HiM2SAM: Enhancing SAM2 with Hierarchical Motion Estimation and Memory Optimization towards Long-term Tracking [48.52769927193921]
  This paper presents enhancements to the SAM2 framework for video object tracking task.<n>We introduce a hierarchical motion estimation strategy, combining lightweight linear prediction with selective non-linear refinement to improve tracking accuracy without requiring additional training.
  arXiv  Detail & Related papers  (2025-07-10T10:05:11Z)
• EdgeTAM: On-Device Track Anything Model [65.10032957471824]
  Segment Anything Model (SAM) 2 further extends its capability from image to video inputs through a memory bank mechanism.<n>We aim at making SAM 2 much more efficient so that it even runs on mobile devices while maintaining a comparable performance.<n>We propose EdgeTAM, which leverages a novel 2D Spatial Perceiver to reduce the computational cost.
  arXiv  Detail & Related papers  (2025-01-13T12:11:07Z)
• Underwater Camouflaged Object Tracking Meets Vision-Language SAM2 [60.47622353256502]
  We propose the first large-scale multi-modal underwater camouflaged object tracking dataset, namely UW-COT220.<n>Based on the proposed dataset, this work first evaluates current advanced visual object tracking methods, including SAM- and SAM2-based trackers, in challenging underwater environments.<n>Our findings highlight the improvements of SAM2 over SAM, demonstrating its enhanced ability to handle the complexities of underwater camouflaged objects.
  arXiv  Detail & Related papers  (2024-09-25T13:10:03Z)
• SOOD++: Leveraging Unlabeled Data to Boost Oriented Object Detection [68.18620488664187]
  We propose a simple yet effective Semi-supervised Oriented Object Detection method termed SOOD++.<n> Specifically, we observe that objects from aerial images usually have arbitrary orientations, small scales, and dense distribution.<n>Extensive experiments conducted on various oriented object under various labeled settings demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2024-07-01T07:03:51Z)
• RTracker: Recoverable Tracking via PN Tree Structured Memory [71.05904715104411]
  We propose a recoverable tracking framework, RTracker, that uses a tree-structured memory to dynamically associate a tracker and a detector to enable self-recovery. Specifically, we propose a Positive-Negative Tree-structured memory to chronologically store and maintain positive and negative target samples. Our core idea is to use the support samples of positive and negative target categories to establish a relative distance-based criterion for a reliable assessment of target loss.
  arXiv  Detail & Related papers  (2024-03-28T08:54:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.