A Benchmark and Simulator for UAV Tracking
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The dataset in [46] contains 110k frames for object tracking from an aerial view.
“The dataset prepared in [46] contains 110k frames to the purpose of object tracking from an aerial view.”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [46]
The dataset in [46] was acquired at low UAV altitude, with moving camera, and different shooting angles due to UAV motion.
“This dataset has been acquired at low UAV altitude, with moving camera, and different shooting angles, due to UAV motion.”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [46]
The dataset in [46] is suitable for drone monitoring but lacks information on dynamic weather and higher altitudes.
“These features make this dataset suitable for drone monitoring, although it lacks information on dynamic weather and higher altitudes.”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [46]
Previous models have to improve nonlinear representation ability by introducing fashion structures, using additional modules, or designing effective online updaters.
“Thus, previous models have to improve the nonlinear representation ability by introducing fashion structures [21, 44, 48], using additional modules [7, 46, 13], designing effective online updaters [2, 47, 10], to name a few.”
From Transformer Tracking (2021) · cites this paper as [13]
Cites
High-Speed Tracking with Kernelized Correlation Filters
Henriques, J. F., Caseiro, R., Martins, P., & Batista, J. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2345390 · Cited by 5,753 · see citing works
Online Object Tracking: A Benchmark
Wu, Y., Lim, J., & Yang, M.-H. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.312 · Cited by 4,138 · see citing works
Wu, Y., Lim, J., & Yang, M.-H. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2388226 · Cited by 3,371 · cited 2× · see citing works
“Video captured from low-altitude UAVs is inherently different from video in popular tracking datasets like OTB50 [43], OTB100 [42], VOT2014, VOT2015, TC128 [26], and ALOV300++ [39].”
BackgroundMethodIncremental Learning for Robust Visual Tracking
Ross, D. A., Lim, J., Lin, R.-S., & Yang, M.-H. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0075-7 · Cited by 3,061 · see citing works
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CARLA: An open urban driving simulator
Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., & Koltun, V. (2017) · arXiv · doi:10.48550/arxiv.1711.03938 · Cited by 3,718 · see citing works
ECO: Efficient Convolution Operators for Tracking
Danelljan, M., Bhat, G., Khan, F. S., & Felsberg, M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.733 · Cited by 2,805 · see citing works
SiamRPN++: Evolution of Siamese Visual Tracking With Very Deep Networks
Li, B., Wu, W., Wang, Q., Zhang, F., Xing, J., & Yan, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00441 · Cited by 2,607 · see citing works
LaSOT: A High-Quality Benchmark for Large-Scale Single Object Tracking
Fan, H., Lin, L., Yang, F., Chu, P., Deng, G., Yu, S., Bai, H., Xu, Y., Liao, C., & Ling, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00552 · Cited by 1,742 · see citing works
“Summaries of existing tracking benchmarks with highquality dense (per frame) annotations, including OTB-2013 [52], OTB-2015 [53], TC-128 [35], NUS-PRO [28], UAV123 [39], UAV20L [39], VOT-2014 [26], VOT-2017 [27] and LaSOT.”
BackgroundMethodInfluentialChen, X., Yan, B., Zhu, J., Wang, D., Yang, X., & Lu, H. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00803 · Cited by 1,588 · see citing works
Detection and tracking meet drones challenge
Zhu, P., Wen, L., Du, D., Bian, X., Fan, H., Hu, Q., & Ling, H. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3119563 · Cited by 1,336 · see citing works
Learning Spatio-Temporal Transformer for Visual Tracking
Yan, B., Peng, H., Fu, J., Wang, D., & Lu, H. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01028 · Cited by 1,280 · see citing works
Discriminative Correlation Filter with Channel and Spatial Reliability
Lukezic, A., Vojir, T., Zajc, L. C., Matas, J., & Kristan, M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.515 · Cited by 1,153 · see citing works
The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking
Du, D., Qi, Y., Yu, H., Yang, Y., Duan, K., Li, G., Zhang, W., Huang, Q., & Tian, Q. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01249-6_23 · Cited by 1,099 · see citing works
Siamese Box Adaptive Network for Visual Tracking
Chen, Z., Zhong, B., Li, G., Zhang, S., & Ji, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00670 · Cited by 1,052 · see citing works
SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines
Xu, Y., Wang, Z., Li, Z., Yuan, Y., & Yu, G. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6944 · Cited by 974 · see citing works
Probabilistic Regression for Visual Tracking
Danelljan, M., Van Gool, L., & Timofte, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00721 · Cited by 798 · see citing works
Deformable Siamese Attention Networks for Visual Object Tracking
Yu, Y., Xiong, Y., Huang, W., & Scott, M. R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00676 · Cited by 495 · see citing works
LaSOT: A High-quality Large-scale Single Object Tracking Benchmark
Fan, H., Bai, H., Lin, L., Yang, F., Chu, P., Deng, G., Yu, S., Harshit, Huang, M., Liu, J., et al. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01387-y · Cited by 360 · see citing works
Overview and methods of correlation filter algorithms in object tracking
Liu, S., Liu, D., Srivastava, G., Połap, D., & Woźniak, M. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-020-00161-4 · Cited by 305 · see citing works
A review on object detection in unmanned aerial vehicle surveillance
Ramachandran, A., & Sangaiah, A. K. (2021) · International Journal of Cognitive Computing in Engineering · doi:10.1016/j.ijcce.2021.11.005 · Cited by 165 · see citing works
A Systematic Review of Drone Based Road Traffic Monitoring System
Bisio, I., Garibotto, C., Haleem, H., Lavagetto, F., & Sciarrone, A. (2022) · IEEE Access · doi:10.1109/access.2022.3207282 · Cited by 121 · see citing works
“The dataset prepared in [46] contains 110k frames to the purpose of object tracking from an aerial view.”
MethodSiam R-CNN: Visual Tracking by Re-Detection
Voigtlaender, P., Luiten, J., Torr, P. H. S., & Leibe, B. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00661 · Cited by 115 · see citing works
Object Detection and Tracking with UAV Data Using Deep Learning
Micheal, A. A., Vani, K., Sanjeevi, S., & Lin, C.-H. (2021) · Journal of the Indian Society of Remote Sensing · doi:10.1007/s12524-020-01229-x · Cited by 58 · see citing works
- Authors
- Mueller, M., Smith, N., & Ghanem, B.
- Affiliations
- King Abdullah University of Science and Technology (SA)
- Corresponding author
- Mueller, Matthias
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 445-461
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2518876086
- Cited by
- 1,944
- References
- 4
- Citations by year
- 2026: 88 · 2025: 215 · 2024: 295 · 2023: 323 · 2022: 277 · 2021: 259 · 2020: 218 · 2019: 156 · 2018: 78 · 2017: 33 · 2016: 2
- FWCI
- 31.10
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology) (submittedVersion)
- Funders
- King Abdullah University of Science and Technology
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- UAV target tracking, aerial video, tracking benchmark, synthetic data generation, low-altitude aerial imagery, ground truth annotation
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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