Context-Aware Correlation Filter Tracking

M. Mueller, N. Smith, B. Ghanem
Conference on Computer Vision and Pattern Recognition (CVPR 2017) [Oral], (2017)

Context-Aware Correlation Filter Tracking


Correlation Filter, Benchmark datasets


​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. We demonstrate with extensive experiments that this framework can significantly improve the performance of many CF trackers with only a modest impact on their frame rate.​




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