Robust Superpixel Tracking
Robust Superpixel Tracking
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DOI：
10.1109/tip.2014.2300823
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发表时间：
2014-04-01
影响因子：
10.6
通讯作者：
Yang, Ming-Hsuan
中科院分区：
文献类型：
--
作者：
Yang, Fan;Lu, Huchuan;Yang, Ming-Hsuan
虽然已经提出了许多算法的对象跟踪证明成功，它仍然是一个具有挑战性的问题，跟踪器处理大的外观变化，由于因素，如规模，运动，形状变形，和遮挡。主要原因之一是缺乏有效的图像表示方案来解释外观变化。大多数跟踪器使用高层次的外观结构或低层次的线索来表示和匹配目标对象。在本文中，我们提出了一种跟踪方法，从中层视觉的角度与超像素捕获的结构信息。我们提出了一种基于超像素的判别外观模型，从而便于跟踪器区分目标和背景与中级线索。然后，通过计算目标-背景置信度图来制定跟踪任务，并通过最大化后验估计来获得最佳候选。实验结果表明，我们的跟踪器是能够处理严重的闭塞和恢复漂移。结合在线更新，所提出的算法表现出良好的对现有的方法进行对象跟踪。此外，该算法有利于前景和背景分割跟踪过程中。
While numerous algorithms have been proposed for object tracking with demonstrated success, it remains a challenging problem for a tracker to handle large appearance change due to factors such as scale, motion, shape deformation, and occlusion. One of the main reasons is the lack of effective image representation schemes to account for appearance variation. Most of the trackers use high-level appearance structure or low-level cues for representing and matching target objects. In this paper, we propose a tracking method from the perspective of midlevel vision with structural information captured in superpixels. We present a discriminative appearance model based on superpixels, thereby facilitating a tracker to distinguish the target and the background with midlevel cues. The tracking task is then formulated by computing a target-background confidence map, and obtaining the best candidate by maximum a posterior estimate. Experimental results demonstrate that our tracker is able to handle heavy occlusion and recover from drifts. In conjunction with online update, the proposed algorithm is shown to perform favorably against existing methods for object tracking. Furthermore, the proposed algorithm facilitates foreground and background segmentation during tracking.