Type
Conference PaperKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionElectrical Engineering Program
Visual Computing Center (VCC)
Date
2017-11-09Online Publication Date
2017-11-09Print Publication Date
2017-07Permanent link to this record
http://hdl.handle.net/10754/626827
Metadata
Show full item recordAbstract
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.Citation
Mueller M, Smith N, Ghanem B (2017) Context-Aware Correlation Filter Tracking. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Available: http://dx.doi.org/10.1109/cvpr.2017.152.Sponsors
This work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research through the VCC funding.Conference/Event name
30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Additional Links
http://ieeexplore.ieee.org/document/8099635/ae974a485f413a2113503eed53cd6c53
10.1109/cvpr.2017.152