RAMC: A Rotation Adaptive Tracker with Motion Constraint for Satellite Video Single-Object Tracking

Abstract

Single-object tracking (SOT) in satellite videos (SVs) is a promising and challenging task in the remote sensing community. In terms of the object itself and the tracking algorithm, the rotation of small-sized objects and tracking drift are common problems due to the nadir view coupled with a complex background. This article proposes a novel rotation adaptive tracker with motion constraint (RAMC) to explore how the hybridization of angle and motion information can be utilized to boost SV object tracking from two branches: rotation and translation. We decouple the rotation and translation motion patterns. The rotation phenomenon is decomposed into the translation solution to achieve adaptive rotation estimation in the rotation branch. In the translation branch, the appearance and motion information are synergized to enhance the object representations and address the tracking drift issue. Moreover, an internal shrinkage (IS) strategy is proposed to optimize the evaluation process of trackers. Extensive experiments on space-born SV datasets captured from the Jilin-1 satellite constellation and International Space Station (ISS) are conducted. The results demonstrate the superiority of the proposed method over other algorithms. With an area under the curve (AUC) of 0.785 and 0.946 in the success and precision plots, respectively, the proposed RAMC achieves optimal performance while running at real-time speed.

Object Tracking Based on Satellite Videos: A Literature Review

“…KCF + multi-frames [6] 2017 KCF with the three-frame-difference [44] 2022 KCF with Multi-feature fusion KCF + target motion [45] 2019 Improved discriminative CF for small objects tracking [46] 2019 High-speed CF-based tracker for object tracking [47] 2019 KCF embedded with motion estimations [48] 2021 Feature fusion, position compensation, local object region KCF + kernel adaptation [49] 2018 VCF using velocity feature and inertia mechanism [50] 2019 Hybrid KCF with histogram of oriented gradient [51] 2021 Rotation-adaptive CF [52] 2022 Rotation-adaptive CF with motion constraint [53] 2022 Spatial-Temporal regularized CF with interacting multiple model [54] 2022 Kernelized CF with color-name features In 2017, Ref. [6] presented a new object tracking method by taking advantage of the KCF and the three-frame-difference method to deal with satellite videos.…”

“…Ref. [52] decoupled the rotation and translation motion patterns and developed a novel rotation adaptive tracker with motion constraints. Experiments based on the Jilin-1 satellite dataset and International Space Station dataset demonstrated the superiority of the proposed method.…”

Coastal Ship Tracking with Memory-Guided Perceptual Network

“…In order to accelerate convergence and enhance robustness, Song et al [13] proposed adding adaptive gain. With the development of deep network technology [14,15], deep features and networks have been widely used in DCF-based tracking methods [9,[16][17][18][19][20].…”