Spatial windowing for correlation filter based visual tracking

Abstract

Correlation filters have been extensively studied to address online visual object tracking task, while achieving favourable performance against the-state-of-the-art methods in various benchmark datasets. Nevertheless, undesired conditions, i.e. partial occlusions or abrupt deformations of the object appearance, severely degrade the performance of correlation filter based tracking methods. To this end, we propose a method for estimating a spatial window for the object observation such that the correlation output of the correlation filter and the windowed observation (i.e. element-wise multiplication of the window and the observation) is improved, especially in these adverse conditions. This approach leads to a performance uplift in the tracking result compared to the classical windowing operation. Moreover, the estimated spatial window of the object patch indicates the object regions that are useful for correlation. We observe a considerable amount of performance increase in the benchmark video sequences by using the proposed visual tracking method.