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MULTI-SENSOR FUSION FOR VIDEO SEGMENTATION

机译：视频分割的多传感器融合

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摘要

Video Segmentation is a fundamental task in computer vision. In many sequences, appearance does not provide enough information to solve the problem. Time-of-Flight cameras provide additional information, namely depth, that can be integrated as an additional feature in a segmentation approach. Typically, the depth information is less sensitive to environment changes. Combined with appearance, this has the potential to be a more robust segmentation method. Motivated by the fact that a simple combination of two information sources might not be the best solution, a novel scheme based on Dempster's theory of evidence is proposed. In contrast to existing methods, the use of Dempster's theory of evidence allows to model inaccuracy and uncertainty. The inaccuracy of the information is influenced by an adaptive weight, that provides a measurement of how reliable a certain information might be. The proposed method is compared with others on a publicly available set of image sequences. The experiments show that the use of the proposed feature fusion improves the segmentation.
机译：视频分割是计算机视觉中的基本任务。在许多情况下，外观无法提供足够的信息来解决问题。飞行时间相机提供附加信息，即深度，可以将其集成为分段方法中的附加功能。通常，深度信息对环境变化不太敏感。结合外观，这有可能成为更强大的分割方法。由于两个信息源的简单组合可能不是最佳解决方案，因此提出了一种基于Dempster证据理论的新颖方案。与现有方法相比，使用Dempster的证据理论可以对不准确性和不确定性进行建模。信息的不准确性受自适应权重的影响，该权重提供了对某些信息可能有多可靠的度量。将所提出的方法与其他可公开使用的图像序列集进行比较。实验表明，所提出的特征融合方法可以改善分割效果。

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