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Improved Video Stabilization using SIFT-Log Polar Technique for Unmanned Aerial Vehicles

机译:利用筛选 - 对无人机航空车辆的筛选 - 日志极性技术改进了视频稳定

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The video taken from unmanned aerial vehicles are usually not stabilized by dynamic conditions of the camera platform. This paper presents an improved solution for video stabilization of aerial vehicles by combining “scale invariant feature transform (SIFT)” with “log-polar transform”. The input video frame is transformed into log-polar that provides extended scale and rotation invariance. Afterward, key-points are extracted using the SIFT method and tracked in consecutive frames to eliminate the unwanted motion in a video. Hence, the presented technique performs better even if the object's scale and orientation vary in video frames when unmanned aerial vehicles change its altitude and orientation during the flight. The results of SIFT and log-polar method are compared with the standard SIFT-ME algorithm to analyze the scale and rotation invariance capability for video stabilization. The proposed technique shows the better capacity to stabilize video even when the UAV changes its attitude and orientation (i.e. scale change tolerance: 44.5% and Rotation tolerance: 53%).
机译:从无人驾驶飞行器拍摄的视频通常不会通过相机平台的动态条件稳定。本文通过将“LOG-PILAR变换”结合“尺度不变特征变换(SIFT)”,提出了一种改进的空中车辆视频稳定解决方案。输入视频帧被转换为逻辑极性,提供扩展刻度和旋转不变性。之后,使用SIFT方法提取键点并在连续帧中跟踪以消除视频中的不需要的运动。因此,即使在无人驾驶飞行器在飞行期间改变其高度和方向时,当物体的比例和方向在视频框架中变化时,所呈现的技术即使在视频框架中变化而变得更好。将Sift和Log-Polar方法的结果与标准SIFT-ME算法进行了比较,分析了视频稳定的刻度和旋转不变性能力。所提出的技术表明,即使无人机改变其姿态和方向(即比例变化公差:44.5%和旋转公差:53%),也表明了稳定视频的能力更好。

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