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For visual tracking, an ideal filter learned by the correlation filter (CF)method should take both discrimination and reliability information. However,existing attempts usually focus on the former one while pay less attention toreliability learning. This may make the learned filter be dominated by theunexpected salient regions on the feature map, thereby resulting in modeldegradation. To address this issue, we propose a novel CF-based optimizationproblem to jointly model the discrimination and reliability information. First,we treat the filter as the element-wise product of a base filter and areliability term. The base filter is aimed to learn the discriminationinformation between the target and backgrounds, and the reliability termencourages the final filter to focus on more reliable regions. Second, weintroduce a local response consistency regular term to emphasize equalcontributions of different regions and avoid the tracker being dominated byunreliable regions. The proposed optimization problem can be solved using thealternating direction method and speeded up in the Fourier domain. We conductextensive experiments on the OTB-2013, OTB-2015 and VOT-2016 datasets toevaluate the proposed tracker. Experimental results show that our trackerperforms favorably against other state-of-the-art trackers.

Correlation Tracking via Joint Discrimination and Reliability Learning