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首页> 外文期刊>International Journal of Innovative Computing Information and Control >TENSOR VOTING BASED OUTLIER REMOVAL FOR GLOBAL MOTION ESTIMATION
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TENSOR VOTING BASED OUTLIER REMOVAL FOR GLOBAL MOTION ESTIMATION

机译：基于Tensor表决的外部运动去除，用于全局运动估计

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摘要

Motion vector based global motion estimation methods have much lower complexity than pixel based ones. Therefore, they are widely used in the compressed domain to estimate the camera motion in video sequences. However, the accuracy of these motion vector based methods largely depends on the quality of the input motion vector field. In real applications, many motion vector outliers are present due to noise or foreground objects. In this paper, a novel tensor voting based motion vector outlier removal method is proposed to improve the quality of the input motion vector field. First, motion vectors are encoded by second order tensors. A 2-D voting process is then used to smooth the motion vector field. Finally, the smoothed motion vector field is compared with the input one to detect outliers. The experimental results on synthetic and real data show the effectiveness of the proposed method.
机译：基于运动矢量的全局运动估计方法的复杂度远低于基于像素的方法。因此，它们被广泛用于压缩域中以估计视频序列中的摄像机运动。但是，这些基于运动矢量的方法的准确性很大程度上取决于输入运动矢量场的质量。在实际应用中，由于噪声或前景物体，存在许多运动矢量离群值。为了提高输入运动矢量场的质量，提出了一种基于张量投票的运动矢量离群值去除方法。首先，运动矢量由二阶张量编码。然后，使用2-D投票过程来平滑运动矢量场。最后，将平滑后的运动矢量场与输入场进行比较，以检测异常值。综合和真实数据的实验结果表明了该方法的有效性。

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