Highly Nonrigid Object Tracking via Patch-Based Dynamic Appearance Modeling
Highly Nonrigid Object Tracking via Patch-Based Dynamic Appearance Modeling
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DOI：
10.1109/tpami.2013.32
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发表时间：
2013-10
影响因子：
23.6
通讯作者：
Junseok Kwon;Kyoung Mu Lee
中科院分区：
文献类型：
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作者：
Junseok Kwon;Kyoung Mu Lee
针对几何外观随时间变化较大的目标，提出了一种新的跟踪算法。为了跟踪这类对象，我们提出了一种基于局部面片的外观模型，并提供了一种高效的在线更新方案，该方案能够自适应地改变面片之间的拓扑。在在线更新过程中，通过分析补丁的似然景观来确定每个补丁的稳健性。基于这种稳健性度量，该方法为每个面片选择最好的特征，并通过随着时间的推移移动、删除或新添加它来修改面片。此外，将粗略的目标分割结果集成到所提出的外观模型中，以进一步增强该模型。由于模型中的局部块可以作为半监督分割任务的良好种子，因此该框架很容易获得分割结果。为了解决斑块数量庞大带来的复杂性问题，将盆地跳跃(BH)采样方法引入到跟踪框架中。在确定性局部优化器的帮助下，BH抽样方法显著降低了计算复杂度。因此，提出的外观模型可以利用足够数量的补丁。实验结果表明，该方法能够准确、稳健地跟踪几何外观变化较大的目标。
A novel tracking algorithm is proposed for targets with drastically changing geometric appearances over time. To track such objects, we develop a local patch-based appearance model and provide an efficient online updating scheme that adaptively changes the topology between patches. In the online update process, the robustness of each patch is determined by analyzing the likelihood landscape of the patch. Based on this robustness measure, the proposed method selects the best feature for each patch and modifies the patch by moving, deleting, or newly adding it over time. Moreover, a rough object segmentation result is integrated into the proposed appearance model to further enhance it. The proposed framework easily obtains segmentation results because the local patches in the model serve as good seeds for the semi-supervised segmentation task. To solve the complexity problem attributable to the large number of patches, the Basin Hopping (BH) sampling method is introduced into the tracking framework. The BH sampling method significantly reduces computational complexity with the help of a deterministic local optimizer. Thus, the proposed appearance model could utilize a sufficient number of patches. The experimental results show that the present approach could track objects with drastically changing geometric appearance accurately and robustly.