Previous Observation Regularized Tracker

Discriminative correlation filter-based trackers get desired performances, but also suffer from unwanted problems such as boundary effect and overfitting. SRDCF tracker uses a spatial penalty matrix to alleviate the effect of boundary effect. STRCF introduces a spatial temporal regularized term to the objective function, which limits the change of filters of two continuous frames. However, both SRDCF and STRCF cannot address the problem of overfitting when targets' appearance changes dramatically. Our work is an extension of SRDCF tracker and STRCF tracker. We use the observations of previous frames to constraint the filter, so that it is able to mitigate the overfitting problem. Furthermore, we apply incremental principal component analysis (IPCA) to extract the principal information of the previous frames' observations. The objective function can be efficiently solved using the alternating direction method of multipliers (ADMM). Our tracker and 9 state-of-the-art trackers are tested on the OTB-2015 benchmark. The experimental results demonstrate its satisfactory performance.