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Effective Visual Tracking Using Multi-Block and Scale Space Based on Kernelized Correlation Filters

View Article: PubMed Central - PubMed

ABSTRACT

Accurate scale estimation and occlusion handling is a challenging problem in visual tracking. Recently, correlation filter-based trackers have shown impressive results in terms of accuracy, robustness, and speed. However, the model is not robust to scale variation and occlusion. In this paper, we address the problems associated with scale variation and occlusion by employing a scale space filter and multi-block scheme based on a kernelized correlation filter (KCF) tracker. Furthermore, we develop a more robust algorithm using an appearance update model that approximates the change of state of occlusion and deformation. In particular, an adaptive update scheme is presented to make each process robust. The experimental results demonstrate that the proposed method outperformed 29 state-of-the-art trackers on 100 challenging sequences. Specifically, the results obtained with the proposed scheme were improved by 8% and 18% compared to those of the KCF tracker for 49 occlusion and 64 scale variation sequences, respectively. Therefore, the proposed tracker can be a robust and useful tool for object tracking when occlusion and scale variation are involved.

No MeSH data available.


Related in: MedlinePlus

Center location error of each frame. The sequences contain challenging attributes such as scale variation, occlusion, and deformation.
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sensors-17-00433-f011: Center location error of each frame. The sequences contain challenging attributes such as scale variation, occlusion, and deformation.

Mentions: We conducted the experiment using center location error (CLE) to prove the performance of the proposed method. The Graph in Figure 11 shows that the proposed method has a low CLE in sequences containing the attributes of scale variation, occlusion, or deformation.


Effective Visual Tracking Using Multi-Block and Scale Space Based on Kernelized Correlation Filters
Center location error of each frame. The sequences contain challenging attributes such as scale variation, occlusion, and deformation.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5375719&req=5

sensors-17-00433-f011: Center location error of each frame. The sequences contain challenging attributes such as scale variation, occlusion, and deformation.
Mentions: We conducted the experiment using center location error (CLE) to prove the performance of the proposed method. The Graph in Figure 11 shows that the proposed method has a low CLE in sequences containing the attributes of scale variation, occlusion, or deformation.

View Article: PubMed Central - PubMed

ABSTRACT

Accurate scale estimation and occlusion handling is a challenging problem in visual tracking. Recently, correlation filter-based trackers have shown impressive results in terms of accuracy, robustness, and speed. However, the model is not robust to scale variation and occlusion. In this paper, we address the problems associated with scale variation and occlusion by employing a scale space filter and multi-block scheme based on a kernelized correlation filter (KCF) tracker. Furthermore, we develop a more robust algorithm using an appearance update model that approximates the change of state of occlusion and deformation. In particular, an adaptive update scheme is presented to make each process robust. The experimental results demonstrate that the proposed method outperformed 29 state-of-the-art trackers on 100 challenging sequences. Specifically, the results obtained with the proposed scheme were improved by 8% and 18% compared to those of the KCF tracker for 49 occlusion and 64 scale variation sequences, respectively. Therefore, the proposed tracker can be a robust and useful tool for object tracking when occlusion and scale variation are involved.

No MeSH data available.


Related in: MedlinePlus