Academic Journal of Computing & Information Science, 2023, 6(11); doi: 10.25236/AJCIS.2023.061101.
Xiaorong Qiu1,2, Md Gapar Md Johar1, Jacquline Tham1, Lilysuriazna Raya1
1Management & Science University, Selangor, Malaysia
2Wuxi Institute of Technology, Wuxi, China
In response to the shortcomings of using a single feature to describe targets and the limitations of target scale invariance in traditional kernel correlation filter tracking algorithms, this paper proposes a scale adaptive kernel correlation filter algorithm that integrates color attribute features. Using principal component analysis to obtain color attribute features with strong discrimination, in order to reduce computational complexity and achieve color adaptation, and designing scale adaptive filters to dynamically adjust the target scale. Compared with the original kernel correlation filter and its improved algorithm, this method has good adaptability in situations such as occlusion, deformation, rotation, and background cluster interferences.
Computer Vision, Target Tracking, Kernel Correlation Filter, Color Adaptation, Scale Adaptation
Xiaorong Qiu, Md Gapar Md Johar, Jacquline Tham, Lilysuriazna Raya. Target Tracking Based on Improved Kernel Correlation Filter. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 11: 1-8. https://doi.org/10.25236/AJCIS.2023.061101.
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