Auto-Learning Correlation-Filter-Based Target State Estimation for Real-Time UAV Tracking
Abstract
:1. Introduction
1.1. Related Works
1.1.1. Discriminative Correlation Filter
1.1.2. Prior Knowledge to Model Update
1.1.3. Redetect the Lost Target
1.2. Contributions
- We present a novel TSE metric to the community which allows accurate estimation of the target state.
- We introduce a new ALCF tracker upon TSE, which consists of a novel auto-learning strategy, a fast lost-and-found strategy, and an effective regularization term for efficient and robust UAV tracking.
- We propose a deep version model which outperforms numerous recently popular deep framework-based trackers and established the new state-of-the-art on several UAV datasets.
- We provide the community with an optimal hand-drafted feature-correlation filter at over 50 FPS on a single CPU.
2. Method
2.1. Overview
2.2. Target State Estimation (TSE)
2.3. Auto-Learning Correlation Filter
2.3.1. Auto-Learning Strategy
2.3.2. Lost-and-Found Strategy
2.3.3. Model Constrain Regularization
2.4. Model Optimization
2.5. Optimization Solutions
2.5.1. The Solution to Sub-Problem
2.5.2. The Solution to Sub-Problem
2.5.3. Update of Lagrangian Parameter
3. Results and Discussion
3.1. Implementation Details
3.2. Comparison with Hand-Crafted Trackers
3.2.1. Results on UAV123@10fps
3.2.2. Results on VisDrone2018-Test-Dev
3.2.3. Results on DTB70
3.2.4. Results on UAVDT
3.2.5. Average Performance Results
3.2.6. Per-Attribute Evaluation
3.2.7. Visualization
3.2.8. Speed
3.3. Ablation Studies
3.3.1. Effect of Auto-Learning Strategy
3.3.2. Effect of Regularization Term
3.3.3. Effect of the Lost-and-Found Strategy
3.3.4. Effect of the Objective Function
3.3.5. Construction of Target State Estimation
3.3.6. Sensitivity Analysis of Model Constraint Regularization Term
3.4. Extension to Deep Trackers
3.4.1. Results on VisDrone2018-Test-Dev
3.4.2. Results on UAVDT
3.4.3. Results on DTB70
3.4.4. Results on UAV123@10fps
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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ALCF | AutoTrack | ECO-HC | STRCF-HC | ARCF | |
---|---|---|---|---|---|
FPS | 51.3 | 58.3 | 69.4 | 23.2 | 23.3 |
Success | 0.503 | 0.494 | 0.487 | 0.469 | 0.469 |
Precision | 0.724 | 0.723 | 0.696 | 0.672 | 0.697 |
AL | MC | LF | R | Precision | AUC | FPS | |
---|---|---|---|---|---|---|---|
Baseline | 0.600 | 0.432 | 36.94 | ||||
✓ | 0.637 | 0.460 | 45.64 | ||||
✓ | 0.649 | 0.463 | 34.03 | ||||
✓ | 0.648 | 0.465 | 32.38 | ||||
✓ | ✓ | 0.654 | 0.468 | 32.36 | |||
✓ | ✓ | ✓ | 0.667 | 0.473 | 47.89 | ||
✓ | ✓ | 0.654 | 0.469 | 42.44 | |||
✓ | ✓ | 0.650 | 0.464 | 33.70 | |||
✓ | ✓ | 0.652 | 0.474 | 36.52 | |||
✓ | ✓ | ✓ | 0.662 | 0.475 | 29.91 | ||
✓ | ✓ | ✓ | ✓ | 0.689 | 0.492 | 41.31 | |
Baseline+STRCF | 0.638 | 0.457 | 31.87 | ||||
Baseline+MC+Fix | 0.639 | 0.459 | 33.40 |
MDNet [16] | UDT+ [15] | PrDiMP [57] | ATOM [20] | DiMP [55] | SiamRPN [17] | CFNet [58] | Super_DiMP [56] | KYS [54] | HiFT [59] | ALCF Ours | DeepALCF Ours | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Precision | 0.790 | 0.800 | 0.797 | 0.751 | 0.806 | 0.781 | 0.778 | 0.800 | 0.810 | 0.721 | 0.798 | 0.816 |
Success | 0.579 | 0.584 | 0.600 | 0.563 | 0.605 | 0.573 | 0.568 | 0.610 | 0.600 | 0.527 | 0.585 | 0.619 |
Trackers | Venue | Precision | Success |
---|---|---|---|
MDNet [16] | CVPR2016 | 0.725 | 0.464 |
MCCT [50] | CVPR2018 | 0.691 | 0.448 |
ASRCF [24] | CVPR2019 | 0.720 | 0.449 |
TADT [60] | CVPR2019 | 0.700 | 0.441 |
UDT [15] | CVPR2019 | 0.674 | 0.442 |
UDT+ [15] | CVPR2020 | 0.696 | 0.415 |
PrDiMP [57] | CVPR2020 | 0.757 | 0.559 |
fECO [61] | TIP2020 | 0.699 | 0.415 |
fDSTRCF [61] | TIP2020 | 0.677 | 0.454 |
HiFT [59] | ICCV2021 | 0.652 | 0.474 |
LUDT [62] | IJCV2021 | 0.631 | 0.418 |
LUDT+ [62] | IJCV2021 | 0.701 | 0.406 |
ALCF | Ours | 0.725 | 0.456 |
DeepALCF | Ours | 0.748 | 0.534 |
Trackers | Venue | Precision | Success |
---|---|---|---|
HCF [63] | ICCV2015 | 0.616 | 0.415 |
MDNet [16] | CVPR2016 | 0.703 | 0.466 |
CFNet [58] | CVPR2017 | 0.587 | 0.398 |
IBCCF [64] | ICCVW2017 | 0.669 | 0.460 |
CREST [65] | ICCV2017 | 0.650 | 0.452 |
ADNet [66] | CVPR2017 | 0.637 | 0.422 |
DaSiamRPN [21] | ECCV2018 | 0.735 | 0.512 |
UDT+ [15] | CVPR2019 | 0.650 | 0.457 |
MCCT [50] | CVPR2019 | 0.725 | 0.484 |
TADT [60] | CVPR2019 | 0.690 | 0.460 |
ASRCF [24] | CVPR2019 | 0.696 | 0.468 |
KAOT [67] | TMM2020 | 0.692 | 0.469 |
ARTracker [33] | GRSL2022 | 0.752 | 0.588 |
ALCF | Ours | 0.694 | 0.480 |
DeepALCF | Ours | 0.727 | 0.530 |
Trackers | Precision | Success | Trackers | Precision | Success |
---|---|---|---|---|---|
HCF | 0.601 | 0.426 | MDNet | 0.664 | 0.477 |
CFNet | 0.568 | 0.422 | IBCCF | 0.651 | 0.481 |
SiamFC [68] | 0.678 | 0.472 | CREST | 0.600 | 0.445 |
C-COT [69] | 0.704 | 0.502 | DeepSTRCF | 0.680 | 0.499 |
DaSiamRPN | 0.689 | 0.481 | UDT+ | 0.674 | 0.470 |
MCCT | 0.689 | 0.496 | ADNet | 0.647 | 0.422 |
TADT | 0.685 | 0.507 | ASRCF | 0.685 | 0.477 |
ALCF | 0.689 | 0.492 | DeepALCF | 0.712 | 0.516 |
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Bian, Z.; Xu, T.; Chen, J.; Ma, L.; Cai, W.; Li, J. Auto-Learning Correlation-Filter-Based Target State Estimation for Real-Time UAV Tracking. Remote Sens. 2022, 14, 5299. https://doi.org/10.3390/rs14215299
Bian Z, Xu T, Chen J, Ma L, Cai W, Li J. Auto-Learning Correlation-Filter-Based Target State Estimation for Real-Time UAV Tracking. Remote Sensing. 2022; 14(21):5299. https://doi.org/10.3390/rs14215299
Chicago/Turabian StyleBian, Ziyang, Tingfa Xu, Junjie Chen, Liang Ma, Wenjing Cai, and Jianan Li. 2022. "Auto-Learning Correlation-Filter-Based Target State Estimation for Real-Time UAV Tracking" Remote Sensing 14, no. 21: 5299. https://doi.org/10.3390/rs14215299
APA StyleBian, Z., Xu, T., Chen, J., Ma, L., Cai, W., & Li, J. (2022). Auto-Learning Correlation-Filter-Based Target State Estimation for Real-Time UAV Tracking. Remote Sensing, 14(21), 5299. https://doi.org/10.3390/rs14215299