Partial Block Scheme and Adaptive Update Model for Kernelized Correlation Filters-Based Object Tracking
Abstract
:1. Introduction
2. Related Works
Discriminative Correlation Filter
3. Proposed Method
- Partial block separation: separating the partial blocks from the whole block of an object. Partial blocks can be adjusted in size and position according to the parameter.
- Translation estimation: calculating the responses using a kernelized correlation filter of all blocks and then selecting the translation response map.
- Scale estimation: estimating the object scale with the scale space and calculating the scale factor.
- Adaptive model update: model updating with the adaptive learning rate considering the reliability of responses.
3.1. Partial Block Scheme
3.2. Translation Estimation
3.3. Scale Estimation
3.4. Adaptive Update Model
4. Experiments
4.1. Parameters and Experimental Setup
4.2. Quantitative Evaluation
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Jeong, S.; Paik, J. Partial Block Scheme and Adaptive Update Model for Kernelized Correlation Filters-Based Object Tracking. Appl. Sci. 2018, 8, 1349. https://doi.org/10.3390/app8081349
Jeong S, Paik J. Partial Block Scheme and Adaptive Update Model for Kernelized Correlation Filters-Based Object Tracking. Applied Sciences. 2018; 8(8):1349. https://doi.org/10.3390/app8081349
Chicago/Turabian StyleJeong, Soowoong, and Joonki Paik. 2018. "Partial Block Scheme and Adaptive Update Model for Kernelized Correlation Filters-Based Object Tracking" Applied Sciences 8, no. 8: 1349. https://doi.org/10.3390/app8081349