Open Access
Issue |
JNWPU
Volume 42, Number 4, August 2024
|
|
---|---|---|
Page(s) | 726 - 734 | |
DOI | https://doi.org/10.1051/jnwpu/20244240726 | |
Published online | 08 October 2024 |
- KALAL Z, MIKOLAJCZYK K, MATAS J. Tracking-learning-detection[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2011, 34(7): 1409–1422 [Google Scholar]
- CHEN Faling, DING Qinghai, LUO Haibo, et al. Anti-occlusion real time target tracking algorithm employing spatio-temporal context[J]. Infrared and Laser Engineering, 2021, 50(1): 20200105 (in Chinese) [CrossRef] [Google Scholar]
- AKIN O, ERDEM E, ERDEM A, et al. Deformable part-based tracking by coupled global and local correlation filters[J]. Journal of Visual Communication and Image Representation, 2016, 38: 763–774. [Article] [CrossRef] [Google Scholar]
- ONDRUSKA P, POSNER I. Deep tracking: seeing beyond seeing using recurrent neural networks[C]//Thirtieth AAAI Conference on Artificial Intelligence, 2016 [Google Scholar]
- ZHU Z, WANG Q, LI B, et al. Distractor-aware siamese networks for visual object tracking[C]//Proceedings of the European Conference on Computer Vision, 2018: 101–117 [Google Scholar]
- GUPTA D K, GAVVES E, SMEULDERS A W. Tackling occlusion in Siamese tracking with structured dropouts[C]//2020 25th International Conference on Pattern Recognition, 2021: 5804–5811 [Google Scholar]
- DIAO Z. A single target tracking algorithm based on Generative Adversarial Networks[J/OL]. (2019-12-27)[2023-05-08]. [Article] [Google Scholar]
- YUAN D, SHU X, LIU Q, et al. Aligned spatial-temporal memory network for thermal infrared target tracking[J]. IEEE Trans on Circuits and Systems Ⅱ: Express Briefs, 2022, 70(3): 1224–1228 [Google Scholar]
- MA S, YANG Y, CHEN G. AODiMP-TIR: anti-occlusion thermal infrared targetstracker based on SuperDiMP[J]. IET Image Process, 2024, 18: 1780–1795. [Article] [CrossRef] [Google Scholar]
- YAO Y, ATKINS E, JOHNSON-ROBERSON M, et al. BiTrap: bi-directional pedestrian trajectory prediction with multi-modal goal estimation[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 1463–1470. [Article] [CrossRef] [Google Scholar]
- MUELLER M, SMITH N, GHANEM B. A benchmark and simulator for UAV tracking[C]//European Conference on Computer vision, 2016: 445–461 [Google Scholar]
- LIU Q, LI X, HE Z, et al. LSOTB-TIR: a large-scale high-diversity thermal infrared object tracking benchmark[C]//Proceedings of the 28th ACM International Conference on Multimedia, 2020: 3847–3856 [Google Scholar]
- HENRIQUES J F, CASEIRO R, MARTINS P, et al. High-speed tracking with kernelized correlation filters[J]. IEEE Trans on Pattern Analysis And Machine Intelligence, 2014, 37(3): 583–596 [Google Scholar]
- DANELLJAN M, BHAT G, SHAHBAZ KHAN F, et al. ECO: efficient convolution operators for tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 6638–6646 [Google Scholar]
- BERTINETTO L, VALMADRE J, HENRIQUES J F, et al. Fully-convolutional siamese networks for object tracking[C]//European Conference on Computer vision, 2016: 850–865 [Google Scholar]
- LI B, WU W, WANG Q, et al. SiamRPN++: Evolution of siamese visual tracking with very deep networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 4282–4291 [Google Scholar]
- WANG Q, ZHANG L, BERTINETTO L, et al. Fast online object tracking and segmentation: a unifying approach[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 1328–1338 [Google Scholar]
- GUO D, WANG J, CUI Y, et al. SiamCAR: siamese fully convolutional classification and regression for visual tracking[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 6269–6277 [Google Scholar]
- DANELLJAN M, BHAT G, KHAN F S, et al. ATOM: accurate tracking by overlap maximization[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 4660–4669 [Google Scholar]
- BHAT G, DANELLJAN M, GOOL L V, et al. Learning discriminative model prediction for tracking[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019: 6182–6191 [Google Scholar]
- DANELLJAN M, GOOL L V, TIMOFTE R. Probabilistic Regression for Visual Tracking[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 7183–7192 [Google Scholar]
- WU Y, LIM J, YANG M H. Online object tracking: a benchmark[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013: 2411–2418 [Google Scholar]
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