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摘要
Objective: Satellite video is a new type of remote sensing system, which is capable of dynamic video and conventional image capturing. Compared with conventional very-high-resolution (VHR) remote sensing systems, a video satellite observes the Earth with a real-time temporal resolution, which has led to studies in the field of traffic density estimation, object detection, and 3D reconstruction. Satellite video has a strong potential in monitoring traffic, animal migration, and ships entering and leaving ports due to its high temporal resolution. Despite much research in the field of conventional video, relatively minimal work has been performed in object tracking for satellite video. Existing object tracking methods primarily emphasize relatively large objects, such as trains and planes. Several researchers have explored replacing or fusing the motion feature for a more accurate prediction of object position. However, few studies have focused on solving the problem caused by the insufficient amount of information of smaller objects, such as vehicles. Tracking vehicles in satellite video has three main challenges. The main challenge is the small size of the target. While the size of a single frame can be as large as 12 000×4 000 pixels, moving targets, such as cars, can be very small and only occupy 1030 pixels. The second challenge is the lack of clear texture because the vehicle targets contain limited and/or confusing information. The third challenge is that unlike aircraft and ships, vehicles are more likely to appear in situations where the background is complex, which makes tracking the vehicle more challenging. For instance, a vehicle may make quick turns, appear partially to the vehicle, or be marked by instant changes in illumination. Selecting or constructing a single image feature that can handle all the situations mentioned above is difficult. Using multiple complementary image features is proposed by merging them into a unified framework based on a lightweight kernelized correlation filter to tackle these challenges. Method: First, two complementary features with certain invariance and discriminative ability, histogram of gradients (HOG) and raw pixels, are used as descriptors of the target image patch. HOG is tied to edge information of vehicles, such as orientations, offering some discriminative ability. A HOG-based tracker can distinguish targets even when partial occlusion occurs or when illumination or road color changes. However, it would be unable to correctly classify the target from similar shapes in its surroundings, suffering from the problems caused by insufficient information. However, the raw pixel feature describes all contents in the image patch without processing, and more information can be kept without post-processing considering the smaller size of vehicles. It is invariant to the plane motion of a rigid object under low-texture information and to tracking vehicles in terms of orientation changes. However, it fails to track vehicles that are partially occluded or in changes of road color and illumination. A response map merging strategy is proposed to fuse the complementary image features by maintaining two trackers, one using the HOG feature to discriminate the target and the other using the raw pixel feature to improve invariance. In this manner, a peak response may arise at a new position, representing invariance and discriminative ability. Finally, restricted by the insufficient information of the target and the discriminative ability of the observation model, responses usually show a multipeak pattern when a disturbance exists. A response distribution criterion-based model updater is exploited to measure the distribution of merged responses. Using a correlation filter facilitates multiple vehicle tracking due to its calculation speed and online training mechanism. Result: Our model is compared with six state-of-the-art correlation filter-based models. Experiments are performed on eight satellite videos captured in different locations worldwide under challenging situations, such as illumination variance, quick turn, partial occlusion, and road color change. Precision plot and success plot are adopted for evaluation. Ablation experiments are performed to demonstrate the efficiency of the method proposed, and quantitative assessments show that our method leads to an effective balance between two trackers. Moreover, visualization results of three videos show how our method achieves a balance between the two trackers. Our method outperforms all the six state-of-the-art methods and achieves a balance between the base trackers. Conclusion: In this paper, a new tracker fused with complementary image features for vehicle tracking in satellite videos is proposed. To overcome the difficulties posed by the small size of the target and the lack of texture and complex background in satellite video tracking, combining the use of HOG and raw pixel features is proposed by merging the response maps of the two trackers to increase their discriminative and invariance abilities. Experiments on eight satellite videos under challenging circumstances demonstrate that our method outperforms other state-of-the-art algorithms in precision plots and success plots.
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