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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.

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Related in: MedlinePlus

Tracking results with state-of-the-art trackers. Top two rows are occlusion sequences and bottom two rows are scale variation sequences. These screen shots were acquired to illustrate situations of occlusions and scale variations.
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sensors-17-00433-f001: Tracking results with state-of-the-art trackers. Top two rows are occlusion sequences and bottom two rows are scale variation sequences. These screen shots were acquired to illustrate situations of occlusions and scale variations.

Mentions: A novel scheme is required to realize efficient and effective performance for visual tracking. The KCF tracker is exceptionally fast, even among other correlation filter-based trackers. Therefore, we apply the multi-block model, which we believe to be more effective based on the KCF tracker for occlusion and scale variation as shown in Figure 1.


Effective Visual Tracking Using Multi-Block and Scale Space Based on Kernelized Correlation Filters
Tracking results with state-of-the-art trackers. Top two rows are occlusion sequences and bottom two rows are scale variation sequences. These screen shots were acquired to illustrate situations of occlusions and scale variations.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

sensors-17-00433-f001: Tracking results with state-of-the-art trackers. Top two rows are occlusion sequences and bottom two rows are scale variation sequences. These screen shots were acquired to illustrate situations of occlusions and scale variations.
Mentions: A novel scheme is required to realize efficient and effective performance for visual tracking. The KCF tracker is exceptionally fast, even among other correlation filter-based trackers. Therefore, we apply the multi-block model, which we believe to be more effective based on the KCF tracker for occlusion and scale variation as shown in Figure 1.

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