Multi-view space object recognition and pose estimation based on kernel regression |
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Authors: | Zhang Haopeng Jiang Zhiguo |
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Institution: | Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China |
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Abstract: | The application of high-performance imaging sensors in space-based space surveillance systems makes it possible to recognize space objects and estimate their poses using vision-based methods. In this paper, we proposed a kernel regression-based method for joint multi-view space object recognition and pose estimation. We built a new simulated satellite image dataset named BUAA-SID 1.5 to test our method using different image representations. We evaluated our method for recognition-only tasks, pose estimation-only tasks, and joint recognition and pose estimation tasks. Experimental results show that our method outperforms the state-of-the-arts in space object recognition, and can recognize space objects and estimate their poses effectively and robustly against noise and lighting conditions. |
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Keywords: | Kernel regression Object recognition Pose estimation Space objects Vision-based |
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