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Scale-adaptive visual tracking with occlusion detection

机译:具有遮挡检测的比例尺自适应视觉跟踪

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摘要

Occlusion is a challenging problem in visual object tracking. Most state-of-the-art trackers may learn the appearance of the occluding target when it becomes occluded by other objects in the scene. This paper proposes a novel approach of detecting occlusion by dividing the target into several patches and computing the peak-to-sidelobe ratio of every response map. Furthermore, our method can calculate the scale factor by finding the maximum response position of each patch. Experiments are performed on several benchmark challenging sequences. And the results show that our approach outperforms state-of-the-art tracking methods while operating in real-time.
机译:在视觉对象跟踪中,遮挡是一个具有挑战性的问题。当目标被场景中的其他物体遮挡时,大多数最新的跟踪器可能会学习该目标的外观。本文提出了一种通过将目标分为几个斑块并计算每个响应图的峰旁瓣比来检测遮挡的新方法。此外,我们的方法可以通过找到每个贴片的最大响应位置来计算比例因子。实验是在几个基准挑战性序列上进行的。结果表明,在实时操作的同时,我们的方法优于最新的跟踪方法。

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