Abstract
As a new type of earth observation satellite approach, video satellites can continuously monitor an area of the Earth and acquire dynamic and abundant information by utilizing video imaging. Hence, video satellites can afford to track various objects of interest on the Earth's surface. Inspired by the capabilities of video satellites, this paper presents a novel method to track fast-moving objects in satellite videos based on the kernelized correlation filter (KCF) embedded with multi-feature fusion and motion trajectory compensation. The contributions of the suggested algorithm are multifold. First, a multi-feature fusion strategy is proposed to describe an object comprehensively, which is challenging for the single-feature approach. Second, a subpixel positioning method is developed to calculate the object’s position and overcome the poor tracking accuracy difficulties caused by inaccurate object localization. Third, introducing an adaptive Kalman filter (AKF) enables compensation and correction of the KCF tracker results and reduces the object’s bounding box drift, solving the moving object occlusion problem. Based on the correlation filtering tracking framework, combined with the above improvement strategies, our algorithm improves the tracking accuracy by at least 17% on average and the success rate by at least 18% on average compared to the KCF algorithm. Hence, our method effectively solves poor object tracking accuracy caused by complex backgrounds and object occlusion. The experimental results utilize satellite videos from the Jilin-1 satellite constellation and highlight the proposed algorithm's appealing tracking results against current state-of-the-art trackers regarding success rate, precision, and robustness metrics.
Highlights
Object tracking is one of the essential methods for dynamic object observation in computer vision, which has been widely used in video surveillance, automatic navigation, artificial intelligence, and other applications [1]
This paper proposes multi-feature fusion and motion trajectory compensation methods to solve poor tracking accuracy
(3) We reduce the estimation error caused by the noise covariance in the randomly selected Kalman filter (KF) by proposing an adaptive adjustment of both covariances using the state discriminant method (SDM) that affords an increased convergence speed to correct the tracking results of the correlation filter quickly
Summary
Object tracking is one of the essential methods for dynamic object observation in computer vision, which has been widely used in video surveillance, automatic navigation, artificial intelligence, and other applications [1]. The purpose of object tracking is to predict the object's size and position in subsequent frames based on the initial frame of a video sequence [2]. With the continuous development of commercial remote sensing satellites, such as the Jilin-1 and Zhuhai-1 satellite constellations, high-resolution videos through video satellites are an affordable method of gazing and observing a specific Earth’s area to obtain rich information. Satellite Technology Co., Ltd and provided 4k high-resolution imagery, capturing detailed information about an area. The satellite imagery was about 1-m resolution at 30 frames per second, and object tracking in such satellite videos has gradually become a new research direction. The corresponding practical applications involve traffic vehicle tracking [3], monitoring seawater [4], monitoring natural disasters [5], military reconnaissance, and precision guidance
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