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.

中文翻译：

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具有信道和空间可靠性的判别相关滤波器跟踪器

短期跟踪是一个开放且具有挑战性的问题，判别相关滤波器 (DCF) 已显示出出色的性能。我们将信道和空间可靠性概念引入 DCF 跟踪，并为其在滤波器更新和跟踪过程中的高效无缝集成提供了一种学习算法。空间可靠性图将过滤器支持调整到适合跟踪的对象部分。这既可以扩大搜索区域，又可以改进对非矩形对象的跟踪。可靠性分数反映了学习滤波器的通道质量，并用作定位中的特征加权系数。实验上，只有两个简单的标准特征集，HoGs 和 colorname，新颖的 CSR-DCF 方法——具有信道和空间可靠性的 DCF——在 VOT 2016、VOT 2015 和 OTB100 上取得了最先进的结果。CSR-DCF 在 CPU 上几乎实时运行。