Learning Scale-Adaptive Tight Correlation Filter for Object Tracking

In this paper, we propose a novel tracking method by formulating tracking as a correlation filtering as well as a ridge regression problem. First, we develop a tight correlation filter-based tracking framework from the signal detection perspective. In this formulation, the correlation filter is set as the same size as the target, which can make full use of the relations of the adjacent image patches and effectively exclude the influence of the background. Specifically, we point out that the novel correlation filter model can be regarded as the ridge regression model which takes into account the different importance of the samples and has the consistent objective with tracking. Second, we focus on the scale variation problem in tracking. By making use of the spatial structure of the correlation filter, the multiscale filter banks can be generated via interpolation to handle the scale estimation problem easily. Third, we present a novel distance importance-based confidence calculation model to determine the final tracking result, which not only makes use of the fine discriminability of the correlation filter but also takes the distance importance of the candidate samples into account to alleviate the impact of similar distractors. Experimental results demonstrate that our method is superior to several state-of-the-art trackers and many other correlation filter-based methods in the benchmark datasets.