Online Spatio-temporal Structural Context Learning for Visual Tracking
Online Spatio-temporal Structural Context Learning for Visual Tracking
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DOI：
10.1007/978-3-642-33765-9_51
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发表时间：
2012-10
期刊：
影响因子：
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通讯作者：
Longyin Wen;Zhaowei Cai;Zhen Lei;Dong Yi;S. Li
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文献类型：
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作者：
Longyin Wen;Zhaowei Cai;Zhen Lei;Dong Yi;S. Li
在无约束的环境中，目标经常改变其外观，随机移动其位置，并被其他物体遮挡，因此视觉跟踪是一个具有挑战性的问题。目标的状态变化在时间和空间上都是连续的，因此本文提出了一种鲁棒的时空结构上下文跟踪器（STT）来完成无约束环境下的跟踪任务。时间上下文捕获目标的历史外观信息，以防止跟踪器在长期跟踪中漂移到背景。空间上下文模型集成了贡献者，这是自动发现的目标周围的关键点，以建立一个支持字段。支持场提供了比目标本身更多的信息，从而可以更精确地预测目标的位置。在各种具有挑战性的数据库上进行的大量实验表明，我们提出的跟踪器优于其他最先进的跟踪器。
Visual tracking is a challenging problem, because the target frequently change its appearance, randomly move its location and get occluded by other objects in unconstrained environments. The state changes of the target are temporally and spatially continuous, in this paper therefore, a robust Spatio-Temporal structural context based Tracker (STT) is presented to complete the tracking task in unconstrained environments. The temporal context capture the historical appearance information of the target to prevent the tracker from drifting to the background in a long term tracking. The spatial context model integrates contributors, which are the key-points automatically discovered around the target, to build a supporting field. The supporting field provides much more information than appearance of the target itself so that the location of the target will be predicted more precisely. Extensive experiments on various challenging databases demonstrate the superiority of our proposed tracker over other state-of-the-art trackers.