Dynamically Spatiotemporal Regularized Correlation Tracking

Recently, due to the high performance, spatially regularized strategy has been widely applied to addressing the issue of boundary effects existed in correlation filter (CF)-based visual tracking. Specifically, it introduces a spatially regularized term to penalize the coefficients of the CFs to be learned depending on their spatial locations. However, the regularization weights are often formed as a fixed Gaussian function, and hence may cause the learned model degenerate due to the inflexible constraints on the ever-changing CFs to be learned over time during tracking. To address this issue, in this paper, we develop a dynamically spatiotemporal regularization model to constrain the CFs to be learned with the ever-changing regularization weights learned from two consecutive frames. The proposed method jointly learns the CFs along with the dynamically spatiotemporal constraint term, which can be efficiently solved in the Fourier domain by the alternative direction method. Extensive evaluations on the popular data sets OTB-100 and VOT-2016 demonstrate that the proposed tracker performs favorably against the baseline tracker and several recently proposed state-of-the-art methods.