supportcorrelationfilters(SCFs) have recently shown great potentials in real-time visualtracking. However, most of existing SCF trackers learn appearance models using the information of current frame, and completel...
详细信息
supportcorrelationfilters(SCFs) have recently shown great potentials in real-time visualtracking. However, most of existing SCF trackers learn appearance models using the information of current frame, and completely neglect inter-frame information. Besides, they still suffer from unwanted boundary effects. In this paper, we proposed a novel spatialtemporalregularizedsupportcorrelation Filter(STRSCF) model, which introduces the spatial weight and temporal regularization term into SCF model. In order to improve the tracking performances, we extend STRSCF to multi-dimensional feature space. In addition, an effective optimization algorithm is developed to solve our STRSCF model in closed form solution. The experimental results on OTB-13 demonstrate that the STRSCF tracker performs superiorly against several state-of-the-art trackers in terms of accuracy and speed.
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