Single Object Tracking in Satellite Videos: Deep Siamese Network Incorporating an Interframe Difference Centroid Inertia Motion Model

Abstract

Satellite video single object tracking has attracted wide attention. The development of remote sensing platforms for earth observation technologies makes it increasingly convenient to acquire high-resolution satellite videos, which greatly accelerates ground target tracking. However, overlarge images with small object size, high similarity among multiple moving targets, and poor distinguishability between the objects and the background make this task most challenging. To solve these problems, a deep Siamese network (DSN) incorporating an interframe difference centroid inertia motion (ID-CIM) model is proposed in this paper. In object tracking tasks, the DSN inherently includes a template branch and a search branch; it extracts the features from these two branches and employs a Siamese region proposal network to obtain the position of the target in the search branch. The ID-CIM mechanism was proposed to alleviate model drift. These two modules build the ID-DSN framework and mutually reinforce the final tracking results. In addition, we also adopted existing object detection datasets for remotely sensed images to generate training datasets suitable for satellite video single object tracking. Ablation experiments were performed on six high-resolution satellite videos acquired from the International Space Station and “Jilin-1” satellites. We compared the proposed ID-DSN results with other 11 state-of-the-art trackers, including different networks and backbones. The comparison results show that our ID-DSN obtained a precision criterion of 0.927 and a success criterion of 0.694 with a frames per second (FPS) value of 32.117 implemented on a single NVIDIA GTX1070Ti GPU.

Single Object Tracking in Satellite Videos: A Correlation Filter-Based Dual-Flow Tracker

“…Discriminative methods are divided into deep learning-based and correlation filter-based. For the former, Siamese network is used for SOT in [12], [13], [51], but the parameters and structures of the networks may need to be adjusted for different SVs. In addition, the deep subnetwork obtains low-resolution representations, which may not be suitable for tracking small objects in SVs [12].…”

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

“…These methods may be numb to a slight rotational issue. To address tracking drift, some approaches [29][30][31]36,37,41,42] have built motion models based on the relatively stable motion patterns of objects in SVs. In [30,36,37,41,42], the authors use the properties of the Kalman filter [43] to predict the object position at low tracking confidence, which attenuates the tracking drift.…”

“…In [30,36,37,41,42], the authors use the properties of the Kalman filter [43] to predict the object position at low tracking confidence, which attenuates the tracking drift. In [29,31], the motion smoothness and centroid inertia models are embedded into the tracking framework to reduce tracking drift. However, most of them [29,31,36,37,41,42] place high demands on positioning accuracy during the initial stage.…”

“…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].…”

A Novel Anti-Drift Visual Object Tracking Algorithm Based on Sparse Response and Adaptive Spatial-Temporal Context-Aware

“…The early discriminative methods mainly focus on training classifiers that utilize statistical machine learning approaches, such as multiple instance learning [13], Boosting [14], support vector machines (SVMs) [15,16], Random forests [17], and Metric learning [18]. Recently, the discriminative correlation filter (DCF) [19][20][21] and deep learning [22][23][24] have emerged in succession. Among them, methods based on DCF [19][20][21] have attracted extensive attention due to their favorable performance in terms of accuracy and robustness in tracking benchmarks [7,8,25] while maintaining high speed.…”