Camera Pose Estimation: What is the computationally efficient approach?

Emad W. William, Hossam E. Abd El Munim, S. Hammad, M. Ghoneima
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Abstract

In this paper we present a system that is suitable for real time camera pose estimation over a video stream, Our approach proves to be computationally efficient for real time applications to calculate the new extrinsic parameters for a moving camera. The system depends only on the camera without the aid of any external sensors or fixed points. Our analysis for the problem shows that the most computationally intensive task is in the feature detectors/descriptors used, for that we will be comparing between 2 feature detectors/descriptors; AKAZE and ORB, that are well known for their good performance in terms of computational cost. Although AKAZE has managed to achieve better results, it comes with higher computational cost yet with substantial gain in terms of quality, this will provide more robust results for our system if compared to ORB. On the other hand, ORB still manages to give enough quality to satisfy our needs and with real-time performance.
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相机姿态估计:什么是计算效率高的方法?
在本文中,我们提出了一种适用于视频流上实时摄像机姿态估计的系统,我们的方法被证明是计算高效的实时应用,用于计算运动摄像机的新外部参数。该系统仅依赖于摄像头,不需要任何外部传感器或固定点的帮助。我们对该问题的分析表明,计算量最大的任务是使用的特征检测器/描述符,因此我们将比较两种特征检测器/描述符;AKAZE和ORB,它们以计算成本方面的良好性能而闻名。虽然AKAZE已经取得了更好的结果,但它带来了更高的计算成本,但在质量方面有了实质性的提高,与ORB相比,这将为我们的系统提供更健壮的结果。另一方面,ORB仍然设法提供足够的质量来满足我们的需求和实时性能。
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