| Citation: | ZHANG B H，CHAI D D，MENG L B，et al. Anti-occlusion target tracking algorithm of UAV based on multiple detection[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（9）：2442-2454 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0693
|
In this research, a multi-detection anti-occlusion target tracking method is suggested to address the issue of poor tracking effect when unmanned aerial vehicle (UAV) targets are occluded by obstacles during the target tracking process. A response confidence discrimination method is designed by fusing various confidence functions under the framework of a spatiotemporal regularization correlation filtering algorithm. In order to understand the occlusion situation, the change of response difference and response gradient are combined together as the basis to judge whether to update the filter template parameters. A scale estimation method combining the block idea and pyramid scale pool is designed to solve the problem of the scale variation of the target in the image. Compared with the other seven algorithms, the proposed algorithm performs well in the UAV data set, and significantly improves the tracking accuracy and success rate in the face of target occlusion, scale change, and fast movement problems in the tracking process. The findings demonstrate that the multi-detection-based anti-occlusion target tracking algorithm has good speed, accuracy, and robustness and can more effectively address the issues of target occlusion and dimension change in the process of UAV target tracking.
| [1] |
BONATTI R, HO C, WANG W S, et al. Towards a robust aerial cinematography platform: Localizing and tracking moving targets in unstructured environments[C]// 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway: IEEE Press, 2020: 229-236.
|
| [2] |
LI R, PANG M J, ZHAO C, et al. Monocular long-term target following on UAVs[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Piscataway: IEEE Press, 2016: 29-37.
|
| [3] |
LUKEŽIC A, VOJÍR T, ZAJC L C, et al. Discriminative correlation filter with channel and spatial reliability[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2017: 4847-4856.
|
| [4] |
BOLME D S, BEVERIDGE J R, DRAPER B A, et al. Visual object tracking using adaptive correlation filters[C]// 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2010: 2544-2550.
|
| [5] |
HENRIQUES J F, CASEIRO R, MARTINS P, et al. Exploiting the circulant structure of tracking-by-detection with kernels[C]//European Conference on Computer Vision. Berlin: Springer, 2012: 702-715.
|
| [6] |
HENRIQUES J F, CASEIRO R, MARTINS P, et al. High-speed tracking with kernelized correlation filters[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3): 583-596. doi: 10.1109/TPAMI.2014.2345390
|
| [7] |
NAM H, HAN B. Learning multi-domain convolutional neural networks for visual tracking[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2016: 4293-4302.
|
| [8] |
DANELLJAN M, BHAT G, KHAN F S, et al. ECO: Efficient convolution operators for tracking[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2017: 6931-6939.
|
| [9] |
WANG L, LIU T, WANG B, et al. Learning hierarchical features for visual object tracking with recursive neural networks[C]// 2019 IEEE International Conference on Image Processing. Piscataway: IEEE Press, 2019: 3088-3092.
|
| [10] |
JIANG M X, HAI T, PAN Z G, et al. Multi-agent deep reinforcement learning for multi-object tracker[J]. IEEE Access, 2019, 7: 32400-32407. doi: 10.1109/ACCESS.2019.2901300
|
| [11] |
潘振福, 朱永利. 使用PSR重检测改进的核相关目标跟踪方法[J]. 计算机工程与应用, 2017, 53(12): 196-202. doi: 10.3778/j.issn.1002-8331.1601-0261
PAN Z F, ZHU Y L. Improvement for tracker with kernelized correlation filters with PSR redetection[J]. Computer Engineering and Applications, 2017, 53(12): 196-202(in Chinese). doi: 10.3778/j.issn.1002-8331.1601-0261
|
| [12] |
侯志强, 韩崇昭. 视觉跟踪技术综述[J]. 自动化学报, 2006, 32(4): 603-617. doi: 10.16383/j.aas.2006.04.016
HOU Z Q, HAN C Z. A survey of visual tracking[J]. Acta Automatica Sinica, 2006, 32(4): 603-617(in Chinese). doi: 10.16383/j.aas.2006.04.016
|
| [13] |
MA H Y, ACTON S T, LIN Z L. SITUP: Scale invariant tracking using average peak-to-correlation energy[J]. IEEE Transactions on Image Processing, 2020, 29: 3546-3557. doi: 10.1109/TIP.2019.2962694
|
| [14] |
LI F, TIAN C, ZUO W M, et al. Learning spatial-temporal regularized correlation filters for visual tracking[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2018: 4904-4913.
|
| [15] |
UZKENT B, SEO Y. EnKCF: Ensemble of kernelized correlation filters for high-speed object tracking[C]// 2018 IEEE Winter Conference on Applications of Computer Vision. Piscataway: IEEE Press, 2018: 1133-1141.
|
| [16] |
LIU S, WANG S, LIU X Y, et al. Fuzzy detection aided real-time and robust visual tracking under complex environments[C]// IEEE Transactions on Fuzzy Systems. Piscataway: IEEE Press, 2020: 90-102.
|
| [17] |
HUANG Z Y, FU C H, LI Y M, et al. Learning aberrance repressed correlation filters for real-time UAV tracking[C]// 2019 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE Press, 2020: 2891-2900.
|
| [18] |
DANELLJAN M, HÄGER G, SHAHBAZ KHAN F, et al. Accurate scale estimation for robust visual tracking[C]// Proceedings of the British Machine Vision Conference 2014. Nottingham: British Machine Vision Association, 2014: 1064-1075.
|
| [19] |
DANELLJAN M, HÄGER G, KHAN F S, et al. Learning spatially regularized correlation filters for visual tracking[C]// 2015 IEEE International Conference on Computer Vision. Piscataway: IEEE Press, 2016: 4310-4318.
|
| [20] |
HENRIQUES J F, CASEIRO R, MARTINS P, et al. Exploiting the circulant structure of tracking-by-detection with kernels[C]//Proceedings of the 12th European conference on Computer Vision—Volume Part IV. New York: ACM, 2012: 702-715.
|
| [21] |
LI Y, ZHU J K. A scale adaptive kernel correlation filter tracker with feature integration[C]//European Conference on Computer Vision. Berlin: Springer, 2015: 254-265.
|
| [22] |
LI Y M, FU C H, DING F Q, et al. AutoTrack: Towards high-performance visual tracking for UAV with automatic spatio-temporal regularization[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2020: 11923-11932.
|
Figures(9) / Tables(4)
Copyright © Journal of Beijing University of Aeronautics and Astronautics
Address: Editorial Department of Journal of Beijing University of Aeronautics and Astronautics, 37 Xueyuan Road, Haidian District, Beijing Post Code: 100191 Email: jbuaa@cqjj8.com
Supported by:
Beijing Renhe Information Technology Co., Ltd.
DownLoad:
