[1]LIN Zhenxian,ZHENG Xingning,WU Chengmao.Robust KCF tracking algorithm combined with fuzzy feature detection[J].CAAI Transactions on Intelligent Systems,2021,16(2):323-329.[doi:10.11992/tis.201912010]
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Robust KCF tracking algorithm combined with fuzzy feature detection

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 2 Page number: 323-329 Column: 学术论文—智能系统 Public date: 2021-03-05
Title:
Robust KCF tracking algorithm combined with fuzzy feature detection
Author(s):
LIN Zhenxian1;  ZHENG Xingning2;  WU Chengmao3
1. School of Science, Xi’an University of Post and Telecommunications, Xi’an 710121, China;
2. School of Communication and Information Engineering, Xi’an University of Post and Telecommunications, Xi’an 710121, China;
3. School of Electronic Engineering, Xi’an University of Post and Telecommunications, Xi’an 710121, China
Keywords:
computer vision; visual tracking; kernel correlation filter (KCF); scale invariant feature transform (SIFT); local binary pattern (LBP); fuzzy feature detector; image definition evaluation function; feature matching
CLC:
TP391.41
DOI:
10.11992/tis.201912010
Abstract:
To address the tracking failure problem caused by image blur in the tracking field, we propose a robust kernelized correlation filter (KCF) tracking algorithm combined with fuzzy feature detection. First, the scale invariant feature transform descriptor is combined with the local binary pattern algorithm to extract the feature points in the fuzzy image, and the circular neighborhood is used to describe the feature points and reduce the dimensions of the feature vector. Thus, the fuzzy feature detector is constructed. Next, the image definition threshold is set. If the definition of the current image is lower than the threshold, fuzzy feature detection is initiated to obtain the tracking target position by matching the feature vectors. Otherwise, the target position is predicted by the traditional KCF algorithm. The test results on the open data sets OTB-2013 and OTB-2015 show that the proposed algorithm can effectively track targets in fuzzy images with higher accuracy than other experimental algorithms
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Last Update: 2021-04-25

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