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SPATIAL WINDOWING FOR CORRELATION FILTER BASED VISUAL TRACKING

2016-09-29
gündoğdu, erhan
Alatan, Abdullah Aydın
Con-elation 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 con-elation 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 con-elation. We observe a considerable amount of performance increase in the benchmark video sequences by using the proposed visual tracking method.