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2016 | OriginalPaper | Buchkapitel

Scale Invariant Kernelized Correlation Filter Based on Gaussian Output

verfasst von : Xiangbo Su, Baochang Zhang, Linlin Yang, Zhigang Li, Yun Yang

Erschienen in: Cloud Computing and Security

Verlag: Springer International Publishing

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Abstract

Kernelized Correlation Filter (KCF) is one of state-of-the-art trackers. However, KCF suffers from the drifting problem due to inaccurate localization caused by the scale variation and wrong candidate selection. In this paper, we propose a new method, named Scale Invariant KCF (SIKCF), which estimates an accurate scale and models the distribution of correlation response to address the template drifting problem. The features of SIKCF consist in: (1) A scale estimation method is used to find an accurate candidate. (2) The correlation response of the target image is reasonably considered to follow a Gaussian distribution, which is used to select the better candidate in tracking procedure. Extensive experiments on the commonly used tracking benchmark show that the proposed method significantly improves the performance of KCF, and achieves a better performance than state-of-the-art trackers.

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Vorheriges Kapitel Learning Based K-Dependence Bayesian Classifiers

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Titel: Scale Invariant Kernelized Correlation Filter Based on Gaussian Output
verfasst von: Xiangbo Su
Baochang Zhang
Linlin Yang
Zhigang Li
Yun Yang
Verlag: Springer International Publishing
Buch: Cloud Computing and Security
Print ISBN: 978-3-319-48673-4

Electronic ISBN: 978-3-319-48674-1

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-48674-1_50

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