Struck: Structured Output Tracking with Kernels

本文提出了一种基于结构化输出预测的自适应视觉对象跟踪框架,该框架允许输出空间直接响应跟踪器的需求,避免了中间分类步骤,并通过使用核化的结构化输出支持向量机和实时应用中的预算机制来实现有效的对象跟踪。

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reference: Struck: Structured Output Tracking with Kernels

hot topic:

tracking-by-detection methods, treated as a classifiction task, use online learning techniques to update the object model

questions:

1) for these updates to happen one needs to convert the estimated object position into a set of labelled training examples, and it is not clear how best to perform this intermediate step.

2) label prediction is not explicitly coupled to the objective for the tracker

solutions:

1)we present a framework for adaptive visual object tracking based on structured output prediction.

2)allowing the output apace to express the needs of the tracker, avoid the need for an intermediate classification step

3)use a kernelized structured output support vector machine

4)for real-time application, we introduce a budgeting mechanism which prevents the unbounded growth in the number of support vectors which otherwise occur during tracking.

转载于:https://www.cnblogs.com/Wanggcong/p/4881792.html

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