Abstract
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 to reliability learning. This may make the learned filter be dominated
by the unexpected salient regions on the feature map, thereby resulting in model degradation. To address this issue, we
propose a novel CF-based optimization problem to jointly
model the discrimination and reliability information. First,
we treat the filter as the element-wise product of a base filter and a reliability term. The base filter is aimed to learn
the discrimination information between the target and backgrounds, and the reliability term encourages the final filter
to focus on more reliable regions. Second, we introduce
a local response consistency regular term to emphasize equal contributions of different regions and avoid the tracker
being dominated by unreliable regions. The proposed optimization problem can be solved using the alternating direction method and speeded up in the Fourier domain. We
conduct extensive experiments on the OTB-2013, OTB-2015
and VOT-2016 datasets to evaluate the proposed tracker.
Experimental results show that our tracker performs favorably against other state-of-the-art trackers