Discriminative Correlation Filter Tracker with Channel and Spatial Reliability
Discriminative Correlation Filter Tracker with Channel and Spatial Reliability
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DOI：
10.1007/s11263-017-1061-3
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发表时间：
2018-07-01
影响因子：
19.5
通讯作者：
Kristan, Matej
中科院分区：
文献类型：
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作者：
Lukezic, Alan;Vojir, Tomas;Kristan, Matej
短时跟踪是一个开放且具有挑战性的问题，判别相关滤波器（DCF）在短时跟踪方面表现出了优异的性能。我们将信道和空间可靠性的概念引入DCF跟踪中，并提供了一种学习算法，使其在滤波器更新和跟踪过程中高效无缝地集成。空间可靠性图将滤波支撑调整到目标适合跟踪的部分。这既可以扩大搜索区域，又可以改善对非矩形对象的跟踪。可靠性分数反映了学习到的滤波器的信道质量，并在定位中用作特征加权系数。在实验中，仅使用两个简单的标准特征集，hog和颜色名称，新型CSR-DCF方法-具有通道和空间可靠性的dcf -在VOT 2016， VOT 2015和OTB100上获得了最先进的结果。CSR-DCF在CPU上接近实时运行。
Short-term tracking is an open and challenging problem for which discriminative correlation filters (DCF) have shown excellent performance. We introduce the channel and spatial reliability concepts to DCF tracking and provide a learning algorithm for its efficient and seamless integration in the filter update and the tracking process. The spatial reliability map adjusts the filter support to the part of the object suitable for tracking. This both allows to enlarge the search region and improves tracking of non-rectangular objects. Reliability scores reflect channel-wise quality of the learned filters and are used as feature weighting coefficients in localization. Experimentally, with only two simple standard feature sets, HoGs and colornames, the novel CSR-DCF method-DCF with channel and spatial reliability-achieves state-of-the-art results on VOT 2016, VOT 2015 and OTB100. The CSR-DCF runs close to real-time on a CPU.