Correlation Filter of Multiple Candidates Match for Anti-Obscure Tracking in Unmanned Aerial Vehicle Scenario
Abstract
:1. Introduction
- We propose a multi-template matching strategy instead of the traditional single-template matching strategy to locate targets.
- We introduce the self-attention mechanism to enhance the extracted candidate feature descriptions and improve the matching accuracy.
2. Related Works
2.1. Discriminative Target Tracking Model
2.2. Self-Attention Mechanism
2.3. Multi-Target Tracking
3. Materials and Methods
3.1. Algorithmic Architecture
Base Tracking Module
3.2. Generation of Candidate Targets and Feature Descriptors
3.3. Target Position Determination
3.4. Experimental Dataset and Evaluation Index
4. Results
4.1. Experimental Environment and Parameters
4.2. OTB Data Set Evaluation Results and Analysis
4.3. UAV123 Dataset Evaluation Results and Analysis
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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All Sequences | Illumination Change | Occlusion | Motion Blur | Fast Moving | Out of View | Low Resolution | Tracking Speed | |
---|---|---|---|---|---|---|---|---|
LMCF | 0.800 | 0.737 | 0.810 | 0.660 | 0.691 | 0.702 | 0.545 | 7FPS |
SRDCF | 0.781 | 0.697 | 0.790 | 0.763 | 0.706 | 0.703 | 0.524 | 8FPS |
ARCF-H | 0.751 | 0.738 | 0.761 | 0.743 | 0.733 | 0.673 | 0.536 | 8FPS |
Staple | 0.738 | 0.692 | 0.732 | 0.628 | 0.605 | 0.586 | 0.499 | 8FPS |
ASRCF | 0.692 | 0.701 | 0.659 | 0.624 | 0.681 | 0.514 | 0.503 | 7FPS |
FDSST | 0.673 | 0.679 | 0.646 | 0.528 | 0.501 | 0.513 | 0.497 | 8FPS |
TLD | 0.521 | 0.460 | 0.468 | 0.482 | 0.473 | 0.516 | 0.327 | 5FPS |
CSK | 0.443 | 0.388 | 0.404 | 0.336 | 0.380 | 0.410 | 0.397 | 9FPS |
MCMCF | 0.813 | 0.744 | 0.832 | 0.647 | 0.700 | 0.711 | 0.513 | 5FPS |
All Sequences | Illumination Change | Occlusion | Motion Blur | Fast Moving | Out of View | Low Resolution | Tracking Speed | |
---|---|---|---|---|---|---|---|---|
LMCF | 0.842 | 0.783 | 0.844 | 0.714 | 0.730 | 0.695 | 0.555 | 7FPS |
SRDCF | 0.783 | 0.761 | 0.845 | 0.790 | 0.741 | 0.683 | 0.520 | 8FPS |
ARCF-H | 0.763 | 0.731 | 0.802 | 0.783 | 0.698 | 0.681 | 0.547 | 8FPS |
Staple | 0.742 | 0.728 | 0.776 | 0.671 | 0.642 | 0.670 | 0.505 | 8FPS |
ASRCF | 0.664 | 0.742 | 0.761 | 0.605 | 0.634 | 0.660 | 0.552 | 7FPS |
FDSST | 0.608 | 0.731 | 0.709 | 0.542 | 0.512 | 0.510 | 0.494 | 8FPS |
TLD | 0.545 | 0.537 | 0.563 | 0.518 | 0.551 | 0.576 | 0.349 | 5FPS |
CSK | 0.481 | 0.481 | 0.500 | 0.342 | 0.381 | 0.379 | 0.411 | 9FPS |
MCMCF | 0.844 | 0.780 | 0.846 | 0.699 | 0.733 | 0.687 | 0.539 | 5FPS |
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Chen, Z.; Zheng, H.; Zhai, X.; Zhang, K.; Xia, H. Correlation Filter of Multiple Candidates Match for Anti-Obscure Tracking in Unmanned Aerial Vehicle Scenario. Mathematics 2023, 11, 163. https://doi.org/10.3390/math11010163
Chen Z, Zheng H, Zhai X, Zhang K, Xia H. Correlation Filter of Multiple Candidates Match for Anti-Obscure Tracking in Unmanned Aerial Vehicle Scenario. Mathematics. 2023; 11(1):163. https://doi.org/10.3390/math11010163
Chicago/Turabian StyleChen, Zhen, Hongyuan Zheng, Xiangping (Bryce) Zhai, Kangliang Zhang, and Hua Xia. 2023. "Correlation Filter of Multiple Candidates Match for Anti-Obscure Tracking in Unmanned Aerial Vehicle Scenario" Mathematics 11, no. 1: 163. https://doi.org/10.3390/math11010163
APA StyleChen, Z., Zheng, H., Zhai, X., Zhang, K., & Xia, H. (2023). Correlation Filter of Multiple Candidates Match for Anti-Obscure Tracking in Unmanned Aerial Vehicle Scenario. Mathematics, 11(1), 163. https://doi.org/10.3390/math11010163