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Highly non-rigid video object tracking using segment-based object candidates

机译：使用基于片段的对象候选对象进行高度非刚性的视频对象跟踪

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摘要

A novel scheme for non-rigid video object tracking using segment-based object candidates is proposed in this paper. Rather than using a conventional bounding box, the tracker is based on segments and considers the target object to be a combination of segments, where the hierarchical hue-saturation-value histogram is extracted as a feature. The objectness method is employed and integrated into the tracker to generate candidates for a similarity measure. Moreover, segment-based motion weights are introduced to give higher weights to candidates with motion consistency. A confidence-collecting scheme is proposed for similar candidates. To validate our method, experiments were conducted using several image sequences with different non-rigid challenges. The experimental results show that the proposed scheme can achieve better performance than other state-of-the-art methods.
机译：提出了一种基于分段对象的非刚性视频目标跟踪的新方案。跟踪器不是使用常规的边界框，而是基于片段，并认为目标对象是片段的组合，其中层次化色相饱和度值直方图被提取为特征。采用客观性方法并将其集成到跟踪器中以生成相似性度量的候选对象。此外，引入了基于段的运动权重，以赋予具有运动一致性的候选者更高的权重。针对相似的候选人，提出了一个信任度收集方案。为了验证我们的方法，使用具有不同非刚性挑战的几个图像序列进行了实验。实验结果表明，与其他现有技术相比，该方案具有更好的性能。

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