Tracking via context-aware regression correlation filter with a spatial–temporal regularization

Abstract

Correlation filters are known to have superior performance in tracking speed. To improve tracking results, we propose a context-aware regression correlation filter with a spatial–temporal regularization for tracking. First, the spatial regularization parameters are computed by the spatial correlation between the target and the surrounding information. Meanwhile, the filter weight distribution is also computed to highlight the target region and suppress the background region. Next, a context-aware model is proposed to adaptively expand the search area of the target with the original regression analysis. Due to the introduction of the context-aware information, the target structure will be changed and the regression model will not be adapted to the Gaussian function. Therefore, an optimized regression objective function is constructed according to the context-aware model. In the tracking process, under rotation, out-of-view, deformation, etc., the tracker still can continue the tracking of subsequent frames according to partial background information. In addition, time variables for online updating of the spatial–temporal model employ the similarity of adjacent frames to achieve more accurate tracking. According to the model form, the alternative direction method of multipliers is used to optimize the model optimization. Extensive experimental results on the OTB-2013 and OTB-2015 dataset object tracking benchmarks prove that the proposed algorithm is superior to other state-of-the-art algorithms in terms of success, accuracy, and robustness.