PSR-deterministic search range penalization method on kernelized correlation filter tracker

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In visual object tracking, exploiting correlation filters to track the target of interest has been flourished, and, by adopting a circulant form of an image or feature descriptors jointed with the convolution theorem, these correlation filter trackers surpass many of the previous state-of-the-art trackers in both tracking speed and stability. Nevertheless, when the appearance of the target object abruptly changes due to occlusion, background cluttering, or viewpoint variation, even the aforementioned correlation filter trackers still tend to fail to compute a reliable correlation output. Concerned with this problem, we propose a method that observes the locational drift of the correlation peak from the desired location. Utilizing this information, we restrict the searching range of the correlation peak to increase the accuracy of the tracker. We verify the performance of the proposed tracker by using 2014 Visual Object Tracking Challenge benchmark dataset.
Publisher
Institute of Electrical and Electronics Engineers Inc.
Issue Date
2016-08-19
Language
English
Citation

13th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2016, pp.856 - 860

DOI
10.1109/URAI.2016.7733995
URI
http://hdl.handle.net/10203/244627
Appears in Collection
ME-Conference Papers(학술회의논문)
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