The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
GlobalTrack: A Simple and Strong Baseline for Long-Term Tracking
Open accessAbstract
A key capability of a long-term tracker is to search for targets in very large areas (typically the entire image) to handle possible target absences or tracking failures. However, currently there is a lack of such a strong baseline for global instance search. In this work, we aim to bridge this gap. Specifically, we propose GlobalTrack, a pure global instance search based tracker that makes no assumption on the temporal consistency of the target's positions and scales. GlobalTrack is developed based on two-stage object detectors, and it is able to perform full-image and multi-scale search of arbitrary instances with only a single query as the guide. We further propose a cross-query loss to improve the robustness of our approach against distractors. With no online learning, no punishment on position or scale changes, no scale smoothing and no trajectory refinement, our pure global instance search based tracker achieves comparable, sometimes much better performance on four large-scale tracking benchmarks (i.e., 52.1% AUC on LaSOT, 63.8% success rate on TLP, 60.3% MaxGM on OxUvA and 75.4% normalized precision on TrackingNet), compared to state-of-the-art approaches that typically require complex post-processing. More importantly, our tracker runs without cumulative errors, i.e., any type of temporary tracking failures will not affect its performance on future frames, making it ideal for long-term tracking. We hope this work will be a strong baseline for long-term tracking and will stimulate future works in this area.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Specifically, previous methods usually consist of multiple components, such as base trackers [9, 50], target verification modules [21], and global detectors [40, 19].”
From Learning Spatio-Temporal Transformer for Visual Tracking (2021) · cites this paper as [19]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.322 · Cited by 29,137
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
Fully-Convolutional Siamese Networks for Object Tracking
Bertinetto, L., Valmadre, J., Henriques, J. F., Vedaldi, A., & Torr, P. H. S. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-48881-3_56 · Cited by 4,546
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 1×
“Over the past decades, significant progress has been made in this area (Wu, Lim, and Yang 2015; Valmadre et al. 2018; Huang, Zhao, and Huang 2018).”
BackgroundKalal, Z., Mikolajczyk, K., & Matas, J. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.239 · Cited by 3,332
High Performance Visual Tracking with Siamese Region Proposal Network
Li, B., Yan, J., Wu, W., Zhu, Z., & Hu, X. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00935 · Cited by 3,023
Learning Multi-domain Convolutional Neural Networks for Visual Tracking
Nam, H., & Han, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.465 · Cited by 2,806
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
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
Flagged first, then by citations
Research works printed but not linked: 1
- Kalal, Z.; Mikolajczyk, K.; and Matas, J. 2012. Tracking-learningdetection. IEEE transactions on pattern analysis and machine intelligence 34(7):1409-1422.
Cited by
Sorted by citations
Items 101–200 of 279
Visual tracking in camera-switching outdoor sport videos: Benchmark and baselines for skiing
Dunnhofer, M., & Micheloni, C. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.103978 · Cited by 14
Transformer-based moving target tracking method for Unmanned Aerial Vehicle
Sun, N., Zhao, J., Wang, G., Liu, C., Liu, P., Tang, X., & Han, J. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105483 · Cited by 14
Cross-Modal Object Tracking: Modality-Aware Representations and a Unified Benchmark
Li, C., Zhu, T., Liu, L., Si, X., Fan, Z., & Zhai, S. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i2.20016 · Cited by 13
Recursive Least-Squares Estimator-Aided Online Learning for Visual Tracking
Gao, J. G., Lu, Y., Qi, X., Kou, Y., Li, B., Li, L., Yu, S., & Hu, W. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3156977 · Cited by 13
Instance-Based Feature Pyramid for Visual Object Tracking
Pi, Z., Shao, Y., Gao, C., & Sang, N. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3113041 · Cited by 12
RGBT tracking via reliable feature configuration
Tu, Z., Pan, W., Duan, Y., Tang, J., & Li, C. (2022) · Science China Information Sciences · doi:10.1007/s11432-020-3160-5 · Cited by 12
GASiam: Graph Attention Based Siamese Tracker for Infrared Anti-UAV
Shi, X., Zhang, Y., Shi, Z., & Zhang, Y. (2022) · 2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA) · doi:10.1109/cvidliccea56201.2022.9824591 · Cited by 12
Correlation Filter Tracker With Sample-Reliability Awareness and Self-Guided Update
Zhou, L., Li, J., Lei, B., Li, W., & Leng, J. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3199325 · Cited by 12
Tracking Skiers from the Top to the Bottom
Dunnhofer, M., Sordi, L., Martinel, N., & Micheloni, C. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00832 · Cited by 11
Capturing Relevant Context for Visual Tracking
Zhang, Y., Ma, B., Wu, J., Huang, L., & Shen, J. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.3038310 · Cited by 11
Self-citationPaaRPN: Probabilistic anchor assignment with region proposal network for visual tracking
Yang, K., Zhang, H., Zhou, D., & Li, D. (2022) · Information Sciences · doi:10.1016/j.ins.2022.03.070 · Cited by 11
Robust Tracking via Fully Exploring Background Prior Knowledge
Wang, Z., Zhou, Z., Chen, F., Xu, J., Pei, W., & Lu, G. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3323702 · Cited by 11
SOTVerse: A User-Defined Task Space of Single Object Tracking
Hu, S., Zhao, X., & Huang, K. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01908-5 · Cited by 11
Self-citationSiamese reciprocal classification and residual regression for robust object tracking
Zhang, J., Miao, M., Zhang, H., Wang, J., Zhang, J., & Qiu, Z. (2022) · Digital Signal Processing · doi:10.1016/j.dsp.2022.103451 · Cited by 10
Pluggable Attack for Visual Object Tracking
Zhao, S., Xu, T., Wu, X., & Kittler, J. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3331899 · Cited by 11
VTST: Efficient Visual Tracking With a Stereoscopic Transformer
Gu, F., Lu, J., Cai, C., Zhu, Q., & Ju, Z. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3360303 · Cited by 10
Hu, S., Lu, J., & Zhou, S. (2024) · International Journal of Network Dynamics and Intelligence · doi:10.53941/ijndi.2024.100006 · Cited by 12
LGTrack: Exploiting Local and Global Properties for Robust Visual Tracking
Liu, C., Zhao, J. W., Bo, C., Li, S., Wang, D., & Lu, H. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3390054 · Cited by 10
Wang, K., Wang, Z., Zhang, X., & Liu, M. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.126202 · Cited by 11
A Contrastive-Augmented Memory Network for Anti-UAV Tracking in TIR Videos
Wang, Z., Hu, Y., Yang, J., Zhou, G., Liu, F., & Liu, Y. (2024) · Remote Sensing · doi:10.3390/rs16244775 · Cited by 10
Deep Correlation Filter Tracking With Shepherded Instance-Aware Proposals
Liang, Y., Wu, Q., Liu, Y., Yan, Y., & Wang, H. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3103601 · Cited by 11
Robust Visual Object Tracking Based on Feature Channel Weighting and Game Theory
Ma, S., Zhao, B., Hou, Z., Yu, W., Pu, L., & Zhang, L. (2023) · International Journal of Intelligent Systems · doi:10.1155/2023/6731717 · Cited by 10
Graph Attention Network for Context-Aware Visual Tracking
Shao, Y., Guo, D., Cui, Y., Wang, Z., Zhang, L., & Zhang, J. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3442290 · Cited by 9
Multi-attention associate prediction network for visual tracking
Sun, X., Sun, H., Jiang, S., Wang, J., Wei, X., & Hu, Z. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128785 · Cited by 9
Keypoint prediction enhanced Siamese networks with attention for accurate visual object tracking
Sakthivel, S. S. K., Joo, Y. H., & Jeong, J. H. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.126237 · Cited by 9
FocusTrack: A Self-Adaptive Local Sampling Algorithm for Efficient Anti-UAV Tracking
Wang, Y., Xu, T., & Li, J. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3562958 · Cited by 9
A Dual-Mode Framework for Robust Long-Term Tracking in Video SAR
Fang, H., Liao, G., Liu, Y., Zeng, C., He, X., & Meng, Q. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3373396 · Cited by 10
Precision in pursuit: a multi-consistency joint approach for infrared anti-UAV tracking
Zhang, J., Lin, Y., Zhou, X., Shi, P., Zhu, X., & Zeng, D. (2024) · The Visual Computer · doi:10.1007/s00371-024-03525-w · Cited by 9
VTT: Long-term Visual Tracking with Transformers
Bian, T., Yang, H., Song, T., Xue, Z., Ma, R., Robertson, N. M., & Guan, H. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412156 · Cited by 9
Deep convolutional correlation iterative particle filter for visual tracking
Mozhdehi, R. J., & Medeiros, H. P. (2022) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2022.103479 · Cited by 10
Learning Adaptive Target-and-Surrounding Soft Mask for Correlation Filter Based Visual Tracking
Zhang, K., Wang, W., Wang, J., Wang, Q., & Li, X. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3108176 · Cited by 9
Language-Guided Dual-Modal Local Correspondence for Single Object Tracking
Yu, J., Cai, Z., Li, Y., Wang, L., Gao, F., & Yu, Y. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3410141 · Cited by 9
Efficient long-term tracking with local–global similar object interference suppression
Zhu, Z., Zhao, X., Tang, K., Zhang, S., Yang, W., & He, W. (2025) · Digital Signal Processing · doi:10.1016/j.dsp.2025.105065 · Cited by 8
Self-citationHierarchical Attention-Enhanced Correlation Refinement for Robust Visual Tracking
Chen, S., Xu, R., Yan, Y., Yang, H., Wang, D., & Zhu, S. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3570076 · Cited by 8
Re-detection and distractor association from a global perspective: A long-term tracking system
Liu, C., Wang, D., & Bo, C. (2023) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2023.108611 · Cited by 8
Discriminative Siamese Complementary Tracker With Flexible Update
Fan, B., Tian, J., Peng, Y., & Tang, Y. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3087347 · Cited by 8
Visual Tracking by Adaptive Continual Meta-Learning
Choi, J., Baik, S., Choi, M., Kwon, J., & Lee, K. M. (2022) · IEEE Access · doi:10.1109/access.2022.3143809 · Cited by 9
M-O SiamRPN with Weight Adaptive Joint MIoU for UAV Visual Localization
Wen, K., Chu, J., Chen, J., Chen, Y., & Cai, J. (2022) · Remote Sensing · doi:10.3390/rs14184467 · Cited by 8
Adaptive sparse attention-based compact transformer for object tracking
Pan, F., Zhao, L., & Wang, C. (2024) · Scientific Reports · doi:10.1038/s41598-024-63028-5 · Cited by 7
Visual tracking based on spatiotemporal transformer and fusion sequences
Wu, R., Liu, Y., Wang, X., & Yang, P. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105107 · Cited by 8
Visual Object Tracking With Multi-Frame Distractor Suppression
Han, Y., Cai, M., Wu, J., Bai, Z., Zhuo, T., Zhang, H., & Zhang, Y. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3489797 · Cited by 8
Visual Tracking based on deformable Transformer and spatiotemporal information
Wu, R., Wen, X., Yuan, L., Xu, H., & Liu, Y. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107269 · Cited by 7
Light Siamese Network for Long-Term Onboard Aerial Tracking
Yang, X. H., Huang, J., Liao, Y., Song, Y., Zhou, Y., & Yang, J. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3397916 · Cited by 7
Temporal relation transformer for robust visual tracking with dual-memory learning
Nie, G., Wang, X., Yan, Z., Xu, X., & Liu, B. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112229 · Cited by 7
Discriminative visual tracking via spatially smooth and steep correlation filters
Wang, W., Zhang, K., Lv, M., & Wang, J. (2021) · Information Sciences · doi:10.1016/j.ins.2021.07.030 · Cited by 7
Channel Exchanging for RGB-T Tracking
Zhao, L., Zhu, M. S., Ren, H., & Xue, L. (2021) · Sensors · doi:10.3390/s21175800 · Cited by 7
Optimal visual tracking using Wasserstein transport proposals
Hong, J., & Kwon, J. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118251 · Cited by 7
Transformer Sub-Patch Matching for High-Performance Visual Object Tracking
Tang, C., Hu, Q., Zhou, G., Yao, J., Zhang, J., Huang, Y., & Ye, Q. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3264664 · Cited by 8
CST Anti-UAV: A Thermal Infrared Benchmark for Tiny UAV Tracking in Complex Scenes
Xie, B., Zhang, C., Wang, F., Liu, P., Lu, F., Chen, Z., & Hu, W. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00647 · Cited by 13
Object discriminability re-extraction for distractor-aware visual object tracking
Cui, Y., Cheng, Q., Guo, D., Kong, X., Wang, Z., & Zhang, J. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.104075 · Cited by 5
Anchor-Free Tracker Based on Space-Time Memory Network
Han, G., Cao, C., Liu, J., & Kwong, S. (2022) · IEEE Multimedia · doi:10.1109/mmul.2022.3207016 · Cited by 6
Attention meets involution in visual tracking
Hou, Y., Luo, Z., Deng, J., Gao, Y., Huang, K.-K., & Li, W. (2022) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2022.103746 · Cited by 7
Self-citationA global re-detection method based on Siamese network in long-term visual tracking
Hou, Z., Han, R., Ma, J., Ma, S., Yu, W., & Fan, J. (2023) · Journal of Real-Time Image Processing · doi:10.1007/s11554-023-01370-z · Cited by 6
UnifiedTT: Visual tracking with unified transformer
Yu, P., Duan, Z., Guan, S., Li, M., & Deng, S. (2024) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2024.104067 · Cited by 6
Textual Tokens Classification for Multi-Modal Alignment in Vision-Language Tracking
Mao, Z., Wang, Y., Chen, X., & Yan, J. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446122 · Cited by 7
Query-Based Object Visual Tracking with Parallel Sequence Generation
Liu, C., Zhang, B., Bo, C., & Wang, D. (2024) · Sensors · doi:10.3390/s24154802 · Cited by 5
Learning adaptive distractor-aware-suppression appearance model for visual tracking
Zhang, H., Zhu, L., Zhao, Y., Li, F., & Huang, D.-S. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110511 · Cited by 5
Object semantic-guided graph attention feature fusion network for Siamese visual tracking
Zhang, J., Miao, M., Zhang, H., Wang, J., Zhao, Y., Chen, Z., & Qiao, J. (2022) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2022.103705 · Cited by 5
Global Context Attention for Robust Visual Tracking
Choi, J. (2023) · Sensors · doi:10.3390/s23052695 · Cited by 5
Long-term object tracking based on joint tracking and detection strategy with Siamese network
Sun, L., Zhang, J., Yang, Z., Gao, D., & Fan, B. (2024) · Multimedia Systems · doi:10.1007/s00530-024-01366-0 · Cited by 6
Hunt-inspired Transformer for visual object tracking
Zhang, Z., Xue, W., Zhou, Y., Zhang, K., & Chen, S. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110703 · Cited by 5
APR-Net Tracker: Attention Pyramidal Residual Network for Visual Object Tracking
Liu, B., Yuan, D., & Li, X. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3398011 · Cited by 4
Dynamic metric memory network for long-term tracking with spatial-temporal region proposal method
Zhang, H., Fu, W., Yang, X., Qi, R., Wang, X., & Zhang, C. (2025) · Pattern Analysis and Applications · doi:10.1007/s10044-025-01441-w · Cited by 4
Joint visual template and natural language for robust visual tracking
Wang, J., Zhang, H., & Zhang, J. (2022) · Electronics Letters · doi:10.1049/ell2.12610 · Cited by 4
Hou, Z., Zhao, J., Wang, Z., Ma, S., Yu, W., & Fan, J. (2023) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2023.103974 · Cited by 4
A Method of Stable Long-Term Single Object Tracking
Yi, Z., Tong, Z., Zhao, Y., Zhao, Z., & Su, F. (2021) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme51207.2021.9428424 · Cited by 4
Chang, L., Pan, X., Li, D. M., & Fu, Z. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3125965 · Cited by 4
A Dynamic Adjust‐Head Siamese network for object tracking
Qiu, S., Gu, Y., Chen, M., Yuan, Z., Yao, Z., & Zhang, X. (2022) · IET Computer Vision · doi:10.1049/cvi2.12148 · Cited by 4
Visual Tracking With Reinforced Template Updating and Redetection Discriminator
Zhong, S., Sun, Y., Gong, S., Zhou, L., & Xie, G. (2022) · IEEE Journal on Miniaturization for Air and Space Systems · doi:10.1109/jmass.2022.3228339 · Cited by 4
Zhang, Z., Xue, W., Zhang, K., Liu, B., Zhang, C., Liu, J., & Chen, S. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3327486 · Cited by 4
Visual intelligence evaluation techniques for single object tracking： a survey
Hu, S., Zhao, X., & Kaiqi, H. (2024) · Journal of Image and Graphics · doi:10.11834/jig.230498 · Cited by 7
Self-citationA Framework for Long-Term Tracking Based on a Global Proposal Network
Zhang, H., Zhu, B. B., Li, X., & Jiang, Y. (2021) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001421550119 · Cited by 4
CVTOT: A Cross-Video Tiny Object Tracking Method and Dataset for Earth Observation Videos
Li, Z., Yue, P., & Zhang, C. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3630601 · Cited by 3
Target–distractor memory joint tracking algorithm via Credit Allocation Network
Zhang, H., Wang, P., Chen, Z., Zhang, J., & Li, L. (2024) · Machine Vision and Applications · doi:10.1007/s00138-024-01508-4 · Cited by 3
Prototype learning based generic multiple object tracking via point-to-box supervision
Liu, W., Lin, Y., Li, Q., She, Y., Yu, Y., Pan, J., & Gu, J. J. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110588 · Cited by 3
StrongSiamTracker: A Siamese Tracker with Dynamic Global Detection for Robust Anti-UAV Tracking
Cui, X., Liu, W., Kong, L., Li, J., Zhang, C., Zhao, R., Wu, P., & He, S. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00659 · Cited by 3
Enhancing the Scale Adaptation of Global Trackers for Infrared UAV Tracking
Feng, Z., Zhang, W., Pan, E., Liu, D., & Yu, Q. (2025) · Drones · doi:10.3390/drones9070469 · Cited by 3
Ship tracking method for resisting similar shape information under satellite videos
Bai, Y., Lv, J., Wang, C., & Geng, Y. (2022) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.16.026517 · Cited by 3
Auto-Learning Correlation-Filter-Based Target State Estimation for Real-Time UAV Tracking
Bian, Z., Xu, T., Chen, J., Ma, L., Cai, W., & Li, J. (2022) · Remote Sensing · doi:10.3390/rs14215299 · Cited by 3
Siamese network based on global and local feature matching for object tracking
Zhao, Z., Zuo, M., Yu, J., He, X., Song, Y., & Zhai, R. (2022) · Journal of Electronic Imaging · doi:10.1117/1.jei.31.6.063022 · Cited by 3
Spatiotemporal key region transformer for visual tracking
Wu, R., Wen, X., Yuan, L., & Xu, H. (2023) · Complex & Intelligent Systems · doi:10.1007/s40747-023-01040-4 · Cited by 3
Multi-template global re-detection based on Gumbel-Softmax in long-term visual tracking
Hou, Z., Ma, J., Yu, W., Yang, Z., Ma, S., & Fan, J. (2023) · Applied Intelligence · doi:10.1007/s10489-023-04584-7 · Cited by 3
A review of visible single target tracking based on Siamese networks
Yin, W., Ye, Z., Peng, Y., & Liu, W. (2023) · International Conference on Electronic Communication and Artificial Intelligence (ICECAI) · doi:10.1109/icecai58670.2023.10176667 · Cited by 3
Robust Template Adjustment Siamese Network for Object Visual Tracking
Tang, C., Qin, P., & Zhang, J. (2021) · Sensors · doi:10.3390/s21041466 · Cited by 3
Jinpu, 张. Z., & Yuehuan, 王. W. (2022) · Infrared and Laser Engineering · doi:10.3788/irla20220042 · Cited by 3
A Visual Tracker Offering More Solutions
Zhao, L., Mahmoud, M. A. I., Ren, H., & Zhu, M. S. (2020) · Sensors · doi:10.3390/s20185374 · Cited by 3
Cross-Modal Object Tracking: Modality-Aware Representations and A Unified BenchmarkPreprint
Li, C., Zhu, T., Liu, L., Si, X., Fan, Z., & Zhai, S. (2022) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/cr2gx · Cited by 3
Vision-based UAV Detection under Adverse Weather ConditionsPreprint
Munir, A., Siddiqui, A. J., Anwar, S., El-Maleh, A. H., Khan, A. H., & Rehman, A. (2023) · doi:10.36227/techrxiv.170326734.49346541/v1 · Cited by 3
Zhuang, Z., Yin, M., Gao, Q., Lin, Y., & Hong, X. (2024) · IEEE Access · doi:10.1109/access.2024.3442810 · Cited by 2
COST: Contrastive one-stage transformer for vision-language small object tracking
Zhang, C., Liu, L., Gao, J., Xin, S., Wen, H., Zhou, X., Ge, S., & Wang, Y. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103604 · Cited by 6
A Simple Detector with Frame Dynamics is a Strong Tracker
Peng, C., Wang, C., Zou, M., Li, D., Yang, Z., Dai, Y., Cheng, M., & Li, X. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00660 · Cited by 2
Hu, H., Li, T., Liu, B., & Zeng, H. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3648169 · Cited by 2
Wen, J., Liu, H., & Li, J. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113143 · Cited by 2
SiamBC: Context-Related Siamese Network for Visual Object Tracking
He, X., & Sun, Y. (2022) · IEEE Access · doi:10.1109/access.2022.3192466 · Cited by 2
Long-term tracking with transformer and template update
Zhang, H., Peng, X., & Wang, X. (2022) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-022-00954-4 · Cited by 2
Accurate Object Tracking by Utilizing Diverse Prior Information
Hu, Z., & Lin, X. X. (2023) · IEEE Access · doi:10.1109/access.2023.3243628 · Cited by 2
Zhang, J., Wei, Y., Zhang, J., Jiang, Z., Yu, P., Ma, Y., & Jin, R. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00661 · Cited by 2
Wasserstein approximate bayesian computation for visual tracking
Park, J., & Kwon, J. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108905 · Cited by 2
LPCF: Robust Correlation Tracking via Locality Preserving Tracking Validation
Zhou, Y., Zhang, W., Shi, Y., Wang, Z., Li, F., & Huang, Q. (2020) · Sensors · doi:10.3390/s20236853 · Cited by 2
Zhou, L., Jin, Y. J., Wang, H., Hu, Z., & Zhao, S. (2022) · Signal Processing · doi:10.1016/j.sigpro.2022.108463 · Cited by 2
Metadata
- Authors
- Huang, L., Zhao, X., & Huang, K.
- Affiliations
- Institute of Automation (CN)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2020-04-03
- Volume
- 34
- Issue
- 07
- Pages
- 11037-11044
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2997896013
- Cited by
- 279
- References
- 36
- Citations by year
- 2026: 16 · 2025: 37 · 2024: 58 · 2023: 56 · 2022: 59 · 2021: 43 · 2020: 10
- FWCI
- 12.96
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- www.aaai.org/ (unspecified)
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61602485, 61673375)
- National Key Research and Development Program of China (2016YFB1001005)
- 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 › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- long-term object tracking, distractor robustness
About these checks
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
