Scribble Tracker: A Matting-Based Approach for Robust Tracking
Scribble Tracker: A Matting-Based Approach for Robust Tracking
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DOI：
10.1109/tpami.2011.257
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发表时间：
2012-08-01
影响因子：
23.6
通讯作者：
Wu, Ying
中科院分区：
文献类型：
--
作者：
Fan, Jialue;Shen, Xiaohui;Wu, Ying
模型更新是跟踪中的一个关键问题。在模型自适应中，前景和背景信息的提取不准确会导致模型漂移，降低跟踪性能。求解漂移问题的最直接也是最困难的方法是获得精确的目标边界。我们通过提出一种基于消光和跟踪相结合的模型自适应框架来解决这一问题。在我们的框架中，粗跟踪结果自动为抠图提供充分和准确的涂鸦，这使得抠图适用于跟踪系统。同时，即使在目标变形较大的情况下，抠图结果也能得到精确的目标边界。在此基础上进一步构建短期特征与长期表现相结合的有效模型，并成功更新。该模型可以通过显式推理成功地处理遮挡。大量实验表明，我们的自适应方案在很大程度上避免了模型漂移，显著优于其他判别跟踪模型。
Model updating is a critical problem in tracking. Inaccurate extraction of the foreground and background information in model adaptation would cause the model to drift and degrade the tracking performance. The most direct yet difficult solution to the drift problem is to obtain accurate boundaries of the target. We approach such a solution by proposing a novel model adaptation framework based on the combination of matting and tracking. In our framework, coarse tracking results automatically provide sufficient and accurate scribbles for matting, which makes matting applicable in a tracking system. Meanwhile, accurate boundaries of the target can be obtained from matting results even when the target has large deformation. An effective model combining short-term features and long-term appearances is further constructed and successfully updated based on such accurate boundaries. The model can successfully handle occlusion by explicit inference. Extensive experiments show that our adaptation scheme largely avoids model drift and significantly outperforms other discriminative tracking models.