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Spatially constrained level-set tracking and segmentation of non-rigid objects

机译：空间约束的水平集跟踪和非刚性对象的分割

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摘要

Level-set is a widely used technique in segmentation-based tracking due to its flexibility in handling 2D topological changes and computational efficiency. Most existing level-set models aim at grouping pixels that have similar features into a region, without consideration of the spatial relationship of these pixels. In this paper, we present a novel level-set tracking method that incorporates spatial information to improve the robustness and accuracy of tracking non-rigid objects. Both tracking and segmentation are performed in a unified probabilistic framework, with additional spatial constraints from a part-based model the Hough Forests. In the stage of tracking, the rigid motion of the target object is estimated by rigid registration in both the color space and the Hough voting space. Then in the stage of segmentation, some support points are obtained from back-projection, and guide the level-set evolution to capture the shape deformation. We conduct quantitative evaluation on two recently proposed public benchmarks: a non-rigid object tracking dataset and the CVPR2013 online tracking benchmark, involving 61 sequences in total. The experimental results demonstrate that our tracking method performs comparably to the state-of-the-arts in the CVPR2013 benchmark, while shows significantly improved performance in tracking non-rigid objects. (C) 2016 Elsevier Inc. All rights reserved.
机译：水平集由于其在处理2D拓扑变化和计算效率方面的灵活性，因此在基于分段的跟踪中被广泛使用。大多数现有的水平集模型旨在将具有相似特征的像素分组到一个区域中，而不考虑这些像素的空间关系。在本文中，我们提出了一种新颖的水平集跟踪方法，该方法结合了空间信息以提高跟踪非刚性物体的鲁棒性和准确性。跟踪和分割都是在统一的概率框架中执行的，而基于霍夫森林的基于零件的模型还具有其他空间约束。在跟踪阶段，通过在颜色空间和霍夫投票空间中的刚性配准来估计目标对象的刚性运动。然后在分割阶段，通过反投影获得一些支撑点，并指导水平集的演化以捕获形状变形。我们对两个最近提出的公共基准进行了定量评估：一个非刚性物体跟踪数据集和CVPR2013在线跟踪基准，总共涉及61个序列。实验结果表明，我们的跟踪方法的性能与CVPR2013基准测试中的最新技术相当，同时在跟踪非刚性物体方面显示出显着改善的性能。 （C）2016 Elsevier Inc.保留所有权利。

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