Multi-view space object recognition and pose estimation based on kernel regression
Multi-view space object recognition and pose estimation based on kernel regression
复制标题
基于核回归的多视空间目标识别与位姿估计
DOI：
10.1016/j.cja.2014.03.021
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发表时间：
2014-10-01
影响因子：
5.7
通讯作者：
Jiang Zhiguo
中科院分区：
文献类型：
--
作者：
Zhang Haopeng;Jiang Zhiguo
高性能成像传感器在天基空间监视系统中的应用，使得利用基于视觉的方法识别空间物体并估计其姿态成为可能。本文提出了一种基于核回归的联合多视角空间目标识别与姿态估计方法。我们建立了一个名为BUAA-SID 1.5的新的模拟卫星图像数据集，使用不同的图像表示来测试我们的方法。我们在仅识别任务、仅姿态估计任务以及联合识别和姿态估计任务中评估了我们的方法。实验结果表明，该方法在空间目标识别方面具有优异的性能，能够在噪声和光照条件下有效地识别空间目标并估计其姿态。(C) 2014年由爱思唯尔有限公司（Elsevier Ltd.）代表中航和北航制作和主办。
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. (C) 2014 Production and hosting by Elsevier Ltd. on behalf of CSAA & BUAA.