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Moving Sensor Video Image Processing Enhanced with Elimination of Ego Motion by Global Registration and SIFT

机译:通过全球注册和筛选,通过消除自我运动来增强传感器视频图像处理

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This field of target tracking is relatively mature when the camera is stationary but the moving sensor poses uniquely challenging problems because relative to the camera, everything in the scene appears to be moving. This report will present a robust and efficient video stabilization algorithm based on the Scale Invariant Feature Transform (SIFT) algorithm and global registration. An important comparison will be made to KLT, a widely used feature based tracker, stabilization algorithms. We will demonstrate KLT's deficiencies and SIFT's advantages when confronted with limited features and 3D structures. Finally an implementation of the overall system with real data provided by UAV's from MRLets Technologies Inc will be described and analyzed.
机译:当相机静止时,该目标跟踪领域相对成熟,但是移动传感器由于相对于相机而造成唯一挑战性问题,因此场景中的一切似乎是移动的。本报告将呈现基于尺度不变特征变换(SIFT)算法和全局注册的稳健高效的视频稳定算法。将对KLT进行重要的比较,是基于广泛使用的特征的跟踪器,稳定算法。当面对有限的特点和3D结构时,我们将展示KLT的缺陷和筛选的优势。最后,将描述并分析来自MRLET Technologies Inc的UAV提供的具有实际数据的整体系统的实现。

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