Correlation filter (CF) based trackers have recently gained a lot of popularity due to their impressive performance on benchmark datasets, while maintaining high frame rates. A significant amount of recent research focuses on the incorporation of stronger features for a richer representation of the tracking target. However, this only helps to discriminate the target from background within a small neighborhood. In this paper, we present a framework that allows the explicit incorporation of global context within CF trackers. We reformulate the original optimization problem and provide a closed form solution for single and multi-dimensional features in the primal and dual domain. Extensive experiments demonstrate that this framework significantly improves the performance of many CF trackers with only a modest impact on frame rate.
|Original language||English (US)|
|Title of host publication||2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||9|
|State||Published - Nov 9 2017|