An Improved Kernelized Correlation Filter Based Visual Tracking Method

Abstract

Correlation filter based trackers have received great attention in the field of visual target tracking, which have shown impressive advantages in terms of accuracy, robustness, and speed. However, there are still some challenges that exist in the correlation filter based methods, such as target scale variation and occlusion. To deal with these problems, an improved kernelized correlation filter (KCF) tracker is proposed, by employing the GM(1,1) grey model, the interval template matching method, and multiblock scheme. In addition, a strict template update strategy is presented in the proposed method to accommodate the appearance change and avoid template corruption. Finally, some experiments are conducted. The proposed method is compared with the top state-of-the-art trackers, and all the tracking algorithms are evaluated on the object tracking benchmark. The experimental results demonstrate obvious improvements of the proposed KCF-based visual tracking method.

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“…In addition to program testing with input of some types of videos, the test is also done by comparing with commonly used tracking methods. The method used as a comparison in this study is Kernelized Correlation Filters (KCF) [90][91], which is one of the existing tracking methods in the OpenCV library. KCF is an extension of the existing tracking methods of Boosting and Multiple Instance Learning (MIL).…”