Fusion detection mechanism of robust correlation filtering visual tracking algorithm

HOU Zhingqiang; WANG Shuai; YU Wangsheng; LI Youmou; MA Sugang

doi:10.5768/JAO201940.0502002

Journal of Applied Optics > 2019 > 40(5): 795-804. > DOI: 10.5768/JAO201940.0502002

HOU Zhingqiang, WANG Shuai, YU Wangsheng, LI Youmou, MA Sugang. Fusion detection mechanism of robust correlation filtering visual tracking algorithm[J]. Journal of Applied Optics, 2019, 40(5): 795-804. DOI: 10.5768/JAO201940.0502002

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Fusion detection mechanism of robust correlation filtering visual tracking algorithm

HOU Zhingqiang^{1, 2,},
WANG Shuai^{1, 2},
YU Wangsheng³,
LI Youmou^{1, 2},
MA Sugang^{1, 2}

1.
School of Computer, Xi'an University of Posts and Telecommunications, Xi'an 710121, China
2.
Shaanxi Provincial Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an University of Posts and Telecommunications, Xi'an 710121, China
3.
Air Force Engineering University, Xi'an 710077, China

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Received Date: January 06, 2019
Revised Date: April 10, 2019

Graphical Abstract

Abstract

Abstract

In order to solve the tracking drift problem caused by the correlation filtering visual tracking algorithm in complex scenes, a correlation filtering tracking framework fused with detection mechanism was proposed.A space-time regularization filter was used as a tracker, and a linear kernel correlation filter was used as a detector.When the response diagram obtained by correlating the tracker with the target was a plurality of peaks, the detector was activated to perform correlation matching on multiple peaks to obtain a retest result; meanwhile, a filter model update strategy using average peak correlation energy was used to obtain a more reliable detector, so as to improve the tracking accuracy and algorithm robustness.The experimental results on the OTB2015, Temple color 128 and VOT2016 data platforms show that compared with the tracking algorithms of better performance proposed in recent years, the proposed algorithm has better robustness and accuracy in complex scenes such as target motion blur, similar background interference and illumination changing, and both of the tracking accuracy and the success rate are improved.
- computer vision,
- target tracking,
- correlation filtering,
- multi-peak detection

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References (34)

References

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Fusion detection mechanism of robust correlation filtering visual tracking algorithm