Long-term Object Tracking Based on Improved Kernelized Correlation Filters
[1]Chen Liping,Wang Mingyu,Yang Wenzhu,et al.Long-term Object Tracking Based on Improved Kernelized Correlation Filters[J].Journal of Zhengzhou University (Engineering Science),2020,41(03):27-32.[doi:10.13705/j.issn.1671-6833.2019.02.001]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
41
Number of periods:
2020 03
Page number:
27-32
Column:
Public date:
2020-07-29
- Title:
- Long-term Object Tracking Based on Improved Kernelized Correlation Filters
- CLC:
- -
- Abstract:
- In order to solve the problem of template drift caused by fast moving and object occlusion in long term target tracking, a Long-term Kernelized Correlation Filter (LKCF) based on improved Kern el Correlation Filter (KCF) was proposed. In this paper, we used the kernel correlation filter algorithm(KCF) as the tracking framework and adopted a highly reliability template update strategy to prevent template destruction. Furthermore, we constructed a conditional target re-detection mechanism to restore the false model caused during tracking. Experimental results indicate that the proposed algorithm not only avoid the problem of template drift effectively but also can track targets steadily for a long time.
- References:
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Last Update:
2020-07-29
