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http://dx.doi.org/10.3837/tiis.2019.10.019

A robust Correlation Filter based tracker with rich representation and a relocation component  

Jin, Menglei (Institute of Information Science, Beijing Jiaotong University)
Liu, Weibin (Institute of Information Science, Beijing Jiaotong University)
Xing, Weiwei (School of Software Engineering, Beijing Jiaotong University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.13, no.10, 2019 , pp. 5161-5178 More about this Journal
Abstract
Correlation Filter was recently demonstrated to have good characteristics in the field of video object tracking. The advantages of Correlation Filter based trackers are reflected in the high accuracy and robustness it provides while maintaining a high speed. However, there are still some necessary improvements that should be made. First, most trackers cannot handle multi-scale problems. To solve this problem, our algorithm combines position estimation with scale estimation. The difference from the traditional method in regard to the scale estimation is that, the proposed method can track the scale of the object more quickly and effective. Additionally, in the feature extraction module, the feature representation of traditional algorithms is relatively simple, and furthermore, the tracking performance is easily affected in complex scenarios. In this paper, we design a novel and powerful feature that can significantly improve the tracking performance. Finally, traditional trackers often suffer from model drift, which is caused by occlusion and other complex scenarios. We introduce a relocation component to detect object at other locations such as the secondary peak of the response map. It partly alleviates the model drift problem.
Keywords
Visual tracking; Kernelized Correlation Filter; feature representation; scale estimation; relocation component;
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