Improving Object Tracking with Voting from False Positive Detections
Improving Object Tracking with Voting from False Positive Detections
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DOI：
10.1109/icpr.2014.337
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发表时间：
2014-08
期刊：
影响因子：
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通讯作者：
Vassileios Balntas;Lilian Tang;K. Mikolajczyk
中科院分区：
文献类型：
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作者：
Vassileios Balntas;Lilian Tang;K. Mikolajczyk
上下文在检测和跟踪方面提供了额外的信息，并提出了几项利用上下文的在线训练有素的追踪器的工作。然而，在跟踪过程中，上下文通常被认为是具有与目标显著相关的运动模式的项目。我们提出了一种新的方法，该方法在逐个检测的跟踪中利用上下文，并利用持续的误报检测。真检测以及重复的假阳性充当指向目标位置的指针。这是通过通用的霍夫投票实现的，并纳入了最先进的在线学习框架。所提出的方法在速度和精度方面都表现出了良好的性能，并且在具有挑战性的基准下改善了当前技术水平的结果。
Context provides additional information in detection and tracking and several works proposed online trained trackers that make use of the context. However, the context is usually considered during tracking as items with motion patterns significantly correlated with the target. We propose a new approach that exploits context in tracking-by-detection and makes use of persistent false positive detections. True detection as well as repeated false positives act as pointers to the location of the target. This is implemented with a generalised Hough voting and incorporated into a state-of-the art online learning framework. The proposed method presents good performance in both speed and accuracy and it improves the current state of the art results in a challenging benchmark.