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EFFECTIVENESS OF VIDEO OBJECT SEGMENTATION BASED ON MPEG LIKE MOTION VECTORS FOR 3D DEPTH ESTIMATION

机译:基于MPEG的视频对象分割的有效性,如运动向量3D深度估计

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This paper discusses the effectiveness of video image segmentation based upon macro blocks associated with the motion vectors defined and embedded in MPEG codec, for the purpose of estimating the 3D structure of a moving object in the video scene. Sequential video frames do not necessarily meet the rigidity assumed in the 3D estimation/reconstruction techniques for stereoscopic pair images of epipolar geometry, thus requiring extraction of the moving objects. To compare the effectiveness of the image segmentation, the robust RANSAC algorithm, which calculates the fundamental matrix of epipolar geometry, was used to statistically examine the deviation of epipoles between two consecutive video images. Test was conducted for a video sequence without image segmentation and for the case where a moving object was extracted based on the macro blocks that have a common specified motion vector. The case with the image segmentation was favored in terms of more consistent values of the epipoles as well as the fundamental matrix.
机译:本文讨论了基于与定义和嵌入MPEG编解码器中的运动矢量相关联的宏块的视频图像分割的有效性,以估计视频场景中的移动物体的3D结构。顺序视频框架不一定满足在末极几何形状的立体对图像的3D估计/重建技术中假定的刚度,从而需要提取移动物体。为了比较图像分割的有效性,用于计算末极几何基本矩阵的鲁棒RANSAC算法,用于统计检查两个连续视频图像之间的突出骨骼的偏差。对于没有图像分割的视频序列进行测试,并且对于基于具有公共指定运动向量的宏块提取移动对象的情况。通过图像分割的情况以更常规的骨骺值以及基本矩阵的含义。

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