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首页> 外文期刊>Journal of network and computer applications >Using incremental subspace and contour template for object tracking
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Using incremental subspace and contour template for object tracking

机译：使用增量子空间和轮廓模板进行对象跟踪

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摘要

Object tracking in the presence of appearance variation and occlusion is a hot topic in research, many algorithms were proposed in recent years. Early contour tracking algorithms used particle filter in a high dimensional space. In practice, contour points can move independently, hence contour deformation forms a high dimensional deformation space. As a result, the application of particle filter is calculation expensive. In this paper, we address the problem of tracking contour in complex environments by involving subspace and a contour template. Specifically, our algorithm tracks the global motion and the local contour deformation separately. We track the global motion by weighted distance to subspace, which is adaptive to the complex environment variation by incremental learning, and then use contour model to track local deformation and evolve the contour to the edge points. The experimental results show that our method can track object contour undergoing partially occlusion and shape deforming, which verify the effectiveness of the proposed algorithm.
机译：存在外观变化和遮挡的目标跟踪是研究的热点，近年来提出了许多算法。早期的轮廓跟踪算法在高维空间中使用了粒子过滤器。实际上，轮廓点可以独立移动，因此轮廓变形会形成高维变形空间。结果，粒子过滤器的应用计算昂贵。在本文中，我们通过涉及子空间和轮廓模板来解决在复杂环境中跟踪轮廓的问题。具体来说，我们的算法分别跟踪全局运动和局部轮廓变形。我们通过到子空间的加权距离跟踪全局运动，通过增量学习适应复杂的环境变化，然后使用轮廓模型跟踪局部变形并将轮廓演化到边缘点。实验结果表明，该方法能够跟踪部分遮挡和变形的物体轮廓，验证了所提算法的有效性。

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