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Robust Scale Adaptive Kernel Correlation Filter Tracker With Hierarchical Convolutional Features
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DOI：10.1109/LSP.2016.2582783.png)
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Abstract
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Visual object tracking is a challenging task due to object appearance changes caused by shape deformation, heavy occlusion, background clutters, illumination variation, and camera motion. In this letter, we propose a novel robust algorithm which decomposes the task of tracking into translation and scale estimation. We estimate the translation by using five correlation filters with hierarchical convolutional features which produced multilevel correlation response maps to collaboratively infer the target location. We also calculate the scale variation by another correlation filter with histogram of oriented gradient features at the same time. Extensive experimental results on a large-scale 50 challenging benchmark dataset show that the proposed algorithm achieved outstanding performance against state-of-the-art methods.
Keywords:
Convolutional neural networks (CNN)
correlation filter
scale estimation
visual tracking
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