# 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. rotation adaptive; optical flow remapping; satellite video; single-object tracking 2022143108133108  
and different kernel functions, and it has shown outstanding performance in tracking objects without rotation. Only a few correlation filters [24; 25; 26] have considered rotation. In addition, tracking drift is a drawback of correlation filters, which may cause the sample to drift away from the object. Some algorithms (e.g., SRDCF [27] and CSR-DCF [13]) have been proposed to prevent tracking drift at the expense of high time consumption. Remote sensing observation capabilities have broadened from static images to dynamic videos. In 2013, the SkySat-1 video satellite captured panchromatic videos with a ground sample distance (GSD) of 1.1 m and a frame rate of 30 frames per second (FPS) [28]. In 2016, the International Space Station (ISS) released an ultra-high-definition RGB video with a GSD of 1.0 m and a frame rate of 3 FPS. From 2015 to the present, members of the Jilin-1 satellite constellation produced by China Changchun Satellite Technology Co., Ltd. (Changchun, China) have been launched. Currently, Jilin-1 can capture 30 FPS RGB videos with a GSD of 0.92 m. Video satellites in orbit deliver rich, dynamic information on the Earth's surface and have been successfully used for SOT [29; 30; 31], traffic analysis [32], stereo mapping [33] and river velocity measurement [34]. However, compared with natural video (NV), SOT in satellite video (SV) involves many challenges and can be defined as an emerging subject [35]. The main difficulties are two-fold. 1. The nadir view makes the rotation (in-plane) of the object a common phenomenon, as shown in Figure 1b-d; rotation can induce non-rigid deformation of the object and change the object spatial layout, affecting the performance of the tracking algorithms [36]. 2. The complex background and low contrast between small-sized objects and the background can lead to tracking drift of the algorithms [37], as shown in Figure 1f-h. The KCF [10] has shown promising performance for SOT in SVs [36; 37; 38; 39]. However, HOG-based KCF inherently cannot handle object rotation [40]. The axis-aligned bounding box of the KCF contains more background information, which may cause tracking drift [36; 39].