Modified Siamese Network Based on Feature Enhancement and Dynamic Template for Low-Light Object Tracking in UAV Videos
Abstract
:1. Introduction
- (1)
- To tackle the challenges of low contrast, low brightness, and low signal-to-noise ratio in low-light images, which hinder effective target feature extraction, an enhanced low-light image enhancement algorithm is proposed. An iterative noise filtering framework is developed to suppress high-intensity noise arising from low-light image enhancement and to emphasize key features in low-light images;
- (2)
- To address the issue of appearance changes in low-light tracking tasks, a dynamic template tracking mechanism is proposed, which surpasses the limited adaptability of traditional Siamese networks reliant on static templates to changes in target features. This enhances the tracker’s robustness;
- (3)
- By amalgamating a dynamic template Siamese network framework with a low-light image enhancement algorithm, two primary challenges are surmounted: extracting target features from low-light images and coping with frequent appearance changes in video sequences. Consequently, an object-tracking algorithm suitable for low-light situations is proposed to bolster tracker performance under such conditions.
2. Related Work
2.1. Low-Light Image Enhancement
2.2. Object Tracking
3. Low-Light Object Tracking Algorithm
3.1. Overall Framework
3.2. Low-Light Image Feature Enhancement Module
3.2.1. Image Illumination Enhancement Submodule
3.2.2. Adaptive Image Filtering Denoizing Algorithm
Algorithm 1: Low-light image feature enhancement algorithm. |
3.3. Bounding Box Prediction Network
3.4. Dynamic Template Tracking Mechanism
3.4.1. Template Update Method
3.4.2. Template Quality Judgment Strategy
Algorithm 2: Dynamic template update algorithm. |
4. Experiment and Discussion
4.1. Experimental Details
4.2. Quantitative Analysis
4.2.1. UAVDark135
4.2.2. UAVDark70
4.2.3. DarkTrack2021
4.2.4. NAT2021
4.2.5. NAT2021L
4.3. Qualitative Analysis
- (1)
- bike6: This video sequence involves a person riding a bicycle as the tracking target, with interference from another similar target also riding a bicycle around the target. The first row of the visualization results in Figure 22 shows that other tracking algorithms fail to identify and drift when the two targets are in close proximity. Conversely, the SiamLT_Res50 algorithm proposed in this paper maintains stable tracking of the target.
- (2)
- group1: This video sequence features two crowds of pedestrians, with the tracking target being a pedestrian from the right crowd. The second row of the visualization results in Figure 22 demonstrates that some tracking algorithms lost the target and tracked the interfering target next to it during the tracking process. The SiamLT_Res50 algorithm proposed in this paper has strong anti-interference ability and consistently tracks the correct target.
- (3)
- running: This video sequence shows two running people, with the tracking target being the person on the left. The third row of Figure 22 presents the visualization results, indicating that some algorithms drift and track the wrong target as the relative position of the two targets changes. In contrast, the SiamLT_Res50 algorithm proposed in this paper accurately tracks the correct target throughout the tracking process.
- (4)
- person_22: This video sequence depicts several people walking in different directions, with the tracking target being one of them walking from right to left. The fourth row of visualization results in Figure 22 reveals that some algorithms fail to overcome the challenge of similar objects blocking each other during their movement and drift while tracking. The SiamLT_Res50 algorithm proposed in this paper achieves stable and accurate tracking of the correct target throughout the tracking process.
4.4. Ablaton Study
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Xie, X.; Xi, J.; Yang, X.; Lu, R.; Xia, W. STFTrack: Spatio-Temporal-Focused Siamese Network for Infrared UAV Tracking. Drones 2023, 7, 296. [Google Scholar] [CrossRef]
- Memon, S.A.; Son, H.; Kim, W.G.; Khan, A.M.; Shahzad, M.; Khan, U. Tracking Multiple Unmanned Aerial Vehicles through Occlusion in Low-Altitude Airspace. Drones 2023, 7, 241. [Google Scholar] [CrossRef]
- Yeom, S. Long Distance Ground Target Tracking with Aerial Image-to-Position Conversion and Improved Track Association. Drones 2022, 6, 55. [Google Scholar] [CrossRef]
- Fan, H.; Bai, H.; Lin, L.; Yang, F.; Chu, P.; Deng, G.; Yu, S.; Huang, M.; Liu, J.; Xu, Y.; et al. Lasot: A high-quality large-scale single object tracking benchmark. Int. J. Comput. Vis. 2021, 129, 439–461. [Google Scholar] [CrossRef]
- Huang, L.; Zhao, X.; Huang, K. Got-10k: A large high-diversity benchmark for generic object tracking in the wild. IEEE Trans. Pattern Anal. Mach. Intell. 2019, 43, 1562–1577. [Google Scholar] [CrossRef] [Green Version]
- Real, E.; Shlens, J.; Mazzocchi, S.; Pan, X.; Vanhoucke, V. Youtube-boundingboxes: A large high-precision human-annotated data set for object detection in video. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017; pp. 5296–5305. [Google Scholar]
- Deng, J.; Dong, W.; Socher, R.; Li, L.J.; Li, K.; Li, F.F. Imagenet: A large-scale hierarchical image database. In Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, 20–25 June 2009; pp. 248–255. [Google Scholar]
- Ye, J.; Fu, C.; Cao, Z.; An, S.; Zheng, G.; Li, B. Tracker meets night: A transformer enhancer for UAV tracking. IEEE Robot. Autom. Lett. 2022, 7, 3866–3873. [Google Scholar] [CrossRef]
- Ye, J.; Fu, C.; Zheng, G.; Cao, Z.; Li, B. Darklighter: Light up the darkness for uav tracking. In Proceedings of the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic, 27 September–1 October 2021; pp. 3079–3085. [Google Scholar]
- Rahman, Z.u.; Jobson, D.J.; Woodell, G.A. Retinex processing for automatic image enhancement. J. Electron. Imaging 2004, 13, 100–110. [Google Scholar]
- Fu, X.; Liao, Y.; Zeng, D.; Huang, Y.; Zhang, X.P.; Ding, X. A probabilistic method for image enhancement with simultaneous illumination and reflectance estimation. IEEE Trans. Image Process. 2015, 24, 4965–4977. [Google Scholar] [CrossRef] [PubMed]
- Fu, X.; Zeng, D.; Huang, Y.; Zhang, X.P.; Ding, X. A weighted variational model for simultaneous reflectance and illumination estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016; pp. 2782–2790. [Google Scholar]
- Guo, X.; Li, Y.; Ling, H. LIME: Low-light image enhancement via illumination map estimation. IEEE Trans. Image Process. 2016, 26, 982–993. [Google Scholar] [CrossRef] [PubMed]
- Xu, L.; Yan, Q.; Xia, Y.; Jia, J. Structure extraction from texture via relative total variation. ACM Trans. Graph. (TOG) 2012, 31, 1–10. [Google Scholar] [CrossRef] [Green Version]
- Li, M.; Liu, J.; Yang, W.; Sun, X.; Guo, Z. Structure-revealing low-light image enhancement via robust retinex model. IEEE Trans. Image Process. 2018, 27, 2828–2841. [Google Scholar] [CrossRef]
- Hao, S.; Han, X.; Guo, Y.; Xu, X.; Wang, M. Low-light image enhancement with semi-decoupled decomposition. IEEE Trans. Multimed. 2020, 22, 3025–3038. [Google Scholar] [CrossRef]
- Ren, Y.; Ying, Z.; Li, T.H.; Li, G. LECARM: Low-light image enhancement using the camera response model. IEEE Trans. Circuits Syst. Video Technol. 2018, 29, 968–981. [Google Scholar] [CrossRef]
- Li, B.; Li, Q.; Zeng, Y.; Rong, Y.; Zhang, R. 3D trajectory optimization for energy-efficient UAV communication: A control design perspective. IEEE Trans. Wirel. Commun. 2021, 21, 4579–4593. [Google Scholar] [CrossRef]
- Lu, Z.; Cheng, R.; Jin, Y.; Tan, K.C.; Deb, K. Neural architecture search as multiobjective optimization benchmarks: Problem formulation and performance assessment. arXiv 2023, arXiv:2208.04321. [Google Scholar] [CrossRef]
- Zhang, Y.; Luo, J.; Zhang, Y.; Huang, Y.; Cai, X.; Yang, J.; Mao, D.; Li, J.; Tuo, X.; Zhang, Y. Resolution enhancement for large-scale real beam mapping based on adaptive low-rank approximation. IEEE Trans. Geosci. Remote Sens. 2022, 60, 5116921. [Google Scholar] [CrossRef]
- Jin, B.; Cruz, L.; Gonçalves, N. Deep facial diagnosis: Deep transfer learning from face recognition to facial diagnosis. IEEE Access 2020, 8, 123649–123661. [Google Scholar] [CrossRef]
- Zheng, Q.; Zhao, P.; Li, Y.; Wang, H.; Yang, Y. Spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification. Neural Comput. Appl. 2021, 33, 7723–7745. [Google Scholar] [CrossRef]
- Panareda Busto, P.; Gall, J. Open set domain adaptation. In Proceedings of the IEEE international Conference on Computer Vision, Venice, Italy, 22–29 October 2017; pp. 754–763. [Google Scholar]
- Zhang, Y.; Guo, X.; Ma, J.; Liu, W.; Zhang, J. Beyond brightening low-light images. Int. J. Comput. Vis. 2021, 129, 1013–1037. [Google Scholar] [CrossRef]
- Wang, R.; Zhang, Q.; Fu, C.W.; Shen, X.; Zheng, W.S.; Jia, J. Underexposed photo enhancement using deep illumination estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 15–20 June 2019; pp. 6849–6857. [Google Scholar]
- Yang, W.; Wang, S.; Fang, Y.; Wang, Y.; Liu, J. From fidelity to perceptual quality: A semi-supervised approach for low-light image enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 14–19 June 2020; pp. 3063–3072. [Google Scholar]
- Jiang, Y.; Gong, X.; Liu, D.; Cheng, Y.; Fang, C.; Shen, X.; Yang, J.; Zhou, P.; Wang, Z. Enlightengan: Deep light enhancement without paired supervision. IEEE Trans. Image Process. 2021, 30, 2340–2349. [Google Scholar] [CrossRef]
- Zhang, Y.; Di, X.; Zhang, B.; Wang, C. Self-supervised image enhancement network: Training with low light images only. arXiv 2020, arXiv:2002.11300. [Google Scholar]
- Guo, C.; Li, C.; Guo, J.; Loy, C.C.; Hou, J.; Kwong, S.; Cong, R. Zero-reference deep curve estimation for low-light image enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 14–19 June 2020; pp. 1780–1789. [Google Scholar]
- Liu, R.; Ma, L.; Zhang, J.; Fan, X.; Luo, Z. Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA, 20–25 June 2021; pp. 10561–10570. [Google Scholar]
- Ma, L.; Ma, T.; Liu, R.; Fan, X.; Luo, Z. Toward fast, flexible, and robust low-light image enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA, 18–24 June 2022; pp. 5637–5646. [Google Scholar]
- Hare, S.; Golodetz, S.; Saffari, A.; Vineet, V.; Cheng, M.M.; Hicks, S.L.; Torr, P.H. Struck: Structured output tracking with kernels. IEEE Trans. Pattern Anal. Mach. Intell. 2015, 38, 2096–2109. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kalal, Z.; Mikolajczyk, K.; Matas, J. Tracking-learning-detection. IEEE Trans. Pattern Anal. Mach. Intell. 2011, 34, 1409–1422. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Henriques, J.F.; Caseiro, R.; Martins, P.; Batista, J. High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 2014, 37, 583–596. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Tao, R.; Gavves, E.; Smeulders, A.W. Siamese instance search for tracking. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016; pp. 1420–1429. [Google Scholar]
- Bertinetto, L.; Valmadre, J.; Henriques, J.F.; Vedaldi, A.; Torr, P.H. Fully-convolutional siamese networks for object tracking. In Proceedings of the Computer Vision–ECCV 2016 Workshops, Amsterdam, The Netherlands, 8–10 and 15–16 October 2016; Proceedings, Part II 14. pp. 850–865. [Google Scholar]
- Kiani Galoogahi, H.; Fagg, A.; Lucey, S. Learning background-aware correlation filters for visual tracking. In Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, 22–29 October 2017; pp. 1135–1143. [Google Scholar]
- Yang, T.; Chan, A.B. Learning dynamic memory networks for object tracking. In Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 8–14 September 2018; pp. 152–167. [Google Scholar]
- Wang, Q.; Teng, Z.; Xing, J.; Gao, J.; Hu, W.; Maybank, S. Learning attentions: Residual attentional siamese network for high performance online visual tracking. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–22 June 2018; pp. 4854–4863. [Google Scholar]
- He, A.; Luo, C.; Tian, X.; Zeng, W. A twofold siamese network for real-time object tracking. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–23 June 2018; pp. 4834–4843. [Google Scholar]
- Li, B.; Yan, J.; Wu, W.; Zhu, Z.; Hu, X. High performance visual tracking with siamese region proposal network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–23 June 2018; pp. 8971–8980. [Google Scholar]
- Ren, S.; He, K.; Girshick, R.; Sun, J. Faster r-cnn: Towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst. 2015, 28. [Google Scholar] [CrossRef] [Green Version]
- Li, B.; Wu, W.; Wang, Q.; Zhang, F.; Xing, J.; Yan, J. Siamrpn++: Evolution of siamese visual tracking with very deep networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 15–20 June 2019; pp. 4282–4291. [Google Scholar]
- Wang, Q.; Zhang, L.; Bertinetto, L.; Hu, W.; Torr, P.H. Fast online object tracking and segmentation: A unifying approach. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 15–20 June 2019; pp. 1328–1338. [Google Scholar]
- Xu, Y.; Wang, Z.; Li, Z.; Yuan, Y.; Yu, G. Siamfc++: Towards robust and accurate visual tracking with target estimation guidelines. In Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA, 7–12 February 2020; Volume 34, pp. 12549–12556. [Google Scholar]
- Zhu, Z.; Wang, Q.; Li, B.; Wu, W.; Yan, J.; Hu, W. Distractor-aware siamese networks for visual object tracking. In Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 8–14 September 2018; pp. 101–117. [Google Scholar]
- Chen, Z.; Zhong, B.; Li, G.; Zhang, S.; Ji, R. Siamese box adaptive network for visual tracking. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 13–19 June 2020; pp. 6668–6677. [Google Scholar]
- Guo, D.; Wang, J.; Cui, Y.; Wang, Z.; Chen, S. SiamCAR: Siamese fully convolutional classification and regression for visual tracking. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 13–19 June 2020; pp. 6269–6277. [Google Scholar]
- Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A.N.; Kaiser, Ł.; Polosukhin, I. Attention is all you need. Adv. Neural Inf. Process. Syst. 2017, 30. [Google Scholar] [CrossRef]
- Li, B.; Fu, C.; Ding, F.; Ye, J.; Lin, F. ADTrack: Target-aware dual filter learning for real-time anti-dark UAV tracking. In Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi’an, China, 30 May–5 June 2021; pp. 496–502. [Google Scholar]
- Ye, J.; Fu, C.; Zheng, G.; Paudel, D.P.; Chen, G. Unsupervised domain adaptation for nighttime aerial tracking. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA, 18–24 June 2022; pp. 8896–8905. [Google Scholar]
- Li, B.; Fu, C.; Ding, F.; Ye, J.; Lin, F. All-day object tracking for unmanned aerial vehicle. IEEE Trans. Mob. Comput. 2022, 22, 4515–4529. [Google Scholar] [CrossRef]
- Chen, T.; Ma, K.K.; Chen, L.H. Tri-state median filter for image denoising. IEEE Trans. Image Process. 1999, 8, 1834–1838. [Google Scholar] [CrossRef] [Green Version]
- Buades, A.; Coll, B.; Morel, J.M. A non-local algorithm for image denoising. In Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), San Diego, CA, USA, 20–25 June 2005; Volume 2, pp. 60–65. [Google Scholar]
- Gu, S.; Zhang, L.; Zuo, W.; Feng, X. Weighted nuclear norm minimization with application to image denoising. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 23–28 June 2014; pp. 2862–2869. [Google Scholar]
- Pang, J.; Cheung, G. Graph Laplacian regularization for image denoising: Analysis in the continuous domain. IEEE Trans. Image Process. 2017, 26, 1770–1785. [Google Scholar] [CrossRef] [Green Version]
- Zhang, K.; Zuo, W.; Chen, Y.; Meng, D.; Zhang, L. Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE Trans. Image Process. 2017, 26, 3142–3155. [Google Scholar] [CrossRef] [Green Version]
- Zhang, K.; Zuo, W.; Zhang, L. FFDNet: Toward a fast and flexible solution for CNN-based image denoising. IEEE Trans. Image Process. 2018, 27, 4608–4622. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Guo, S.; Yan, Z.; Zhang, K.; Zuo, W.; Zhang, L. Toward convolutional blind denoising of real photographs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 15–20 June 2019; pp. 1712–1722. [Google Scholar]
- Wang, M.; Liu, Y.; Huang, Z. Large margin object tracking with circulant feature maps. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017; pp. 4021–4029. [Google Scholar]
- Russakovsky, O.; Deng, J.; Su, H.; Krause, J.; Satheesh, S.; Ma, S.; Huang, Z.; Karpathy, A.; Khosla, A.; Bernstein, M.; et al. Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 2015, 115, 211–252. [Google Scholar] [CrossRef] [Green Version]
- Hai, J.; Xuan, Z.; Yang, R.; Hao, Y.; Zou, F.; Lin, F.; Han, S. R2rnet: Low-light image enhancement via real-low to real-normal network. J. Vis. Commun. Image Represent. 2023, 90, 103712. [Google Scholar] [CrossRef]
- Fu, C.; Cao, Z.; Li, Y.; Ye, J.; Feng, C. Siamese anchor proposal network for high-speed aerial tracking. In Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi’an, China, 30 May–5 June 2021; pp. 510–516. [Google Scholar]
- Cao, Z.; Fu, C.; Ye, J.; Li, B.; Li, Y. SiamAPN++: Siamese attentional aggregation network for real-time UAV tracking. In Proceedings of the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic, 27 September–1 October 2021; pp. 3086–3092. [Google Scholar]
- Cao, Z.; Fu, C.; Ye, J.; Li, B.; Li, Y. Hift: Hierarchical feature transformer for aerial tracking. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Virtual, 11–17 October 2021; pp. 15457–15466. [Google Scholar]
LE | AD | DT | APCE | UAVDark135 | DarkTrack2021 | ||
---|---|---|---|---|---|---|---|
Precision | Success | Precision | Success | ||||
✕ | ✕ | ✕ | ✕ | 0.642 | 0.505 | 0.617 | 0.470 |
✓ | ✕ | ✕ | ✕ | 0.657 | 0.510 | 0.630 | 0.478 |
✓ | ✓ | ✕ | ✕ | 0.660 | 0.515 | 0.643 | 0.493 |
✓ | ✓ | ✓ | ✕ | 0.702 | 0.548 | 0.647 | 0.498 |
✓ | ✓ | ✓ | ✓ | 0.707 | 0.550 | 0.659 | 0.505 |
p | 0.1 | 0.148 | 0.2 | 0.3 | 0.4 | 0.5 | 0.6 | 0.7 | 0.8 | 0.9 |
success rate | 0.515 | 0.528 | 0.512 | 0.506 | 0.505 | 0.506 | 0.506 | 0.506 | 0.506 | 0.506 |
precision | 0.709 | 0.724 | 0.705 | 0.698 | 0.698 | 0.700 | 0.700 | 0.700 | 0.700 | 0.700 |
q | 10 | 20 | 30 | 40 | 50 | 60 | 70 | 80 | 90 |
success rate | 0.402 | 0.415 | 0.481 | 0.511 | 0.511 | 0.511 | 0.528 | 0.483 | 0.477 |
precision | 0.622 | 0.637 | 0.652 | 0.702 | 0.702 | 0.709 | 0.724 | 0.676 | 0.667 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Sun, L.; Kong, S.; Yang, Z.; Gao, D.; Fan, B. Modified Siamese Network Based on Feature Enhancement and Dynamic Template for Low-Light Object Tracking in UAV Videos. Drones 2023, 7, 483. https://doi.org/10.3390/drones7070483
Sun L, Kong S, Yang Z, Gao D, Fan B. Modified Siamese Network Based on Feature Enhancement and Dynamic Template for Low-Light Object Tracking in UAV Videos. Drones. 2023; 7(7):483. https://doi.org/10.3390/drones7070483
Chicago/Turabian StyleSun, Lifan, Shuaibing Kong, Zhe Yang, Dan Gao, and Bo Fan. 2023. "Modified Siamese Network Based on Feature Enhancement and Dynamic Template for Low-Light Object Tracking in UAV Videos" Drones 7, no. 7: 483. https://doi.org/10.3390/drones7070483