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 novel 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 features, 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 in real-time on a CPU.

本篇文章介绍了带有空间和通道可靠性概念的建议学习算法的判别相关滤波（DCF）跟踪，其在跟踪非矩形对象和扩大搜索区域方面表现出色。通过基于两个简单标准特征，HoGs 和 Colornames，CSR-DCF 方法在 VOT 2016、VOT 2015 和 OTB100 方面取得了最先进的结果，并可以在 CPU 上实时运行。