Discriminative Correlation Filter with Channel and Spatial Reliability

Alan Lukezic, Tomas Vojir, Luka Cehovin, Jiri Matas, Matej Kristan

Abstract:

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 CSRDCF 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