The DOI matches its Crossref record; 115 of the 115 references it cites were found in a registry or an index.
Siam R-CNN: Visual Tracking by Re-Detection
Open accessAbstract
We present Siam R-CNN, a Siamese re-detection architecture which unleashes the full power of two-stage object detection approaches for visual object tracking. We combine this with a novel tracklet-based dynamic programming algorithm, which takes advantage of re-detections of both the first-frame template and previous-frame predictions, to model the full history of both the object to be tracked and potential distractor objects. This enables our approach to make better tracking decisions, as well as to re-detect tracked objects after long occlusion. Finally, we propose a novel hard example mining strategy to improve Siam R-CNN's robustness to similar looking objects. Siam R-CNN achieves the current best performance on ten tracking benchmarks, with especially strong results for long-term tracking. We make our code and models available at www.vision.rwth-aachen.de/page/siamrcnn.
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 source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“TransT-GOT method performs 2.2% higher than SiamR-CNN in the main AO metric.”
From Transformer Tracking (2021) · cites this paper as [39]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“but SiamR-CNN merely runs less than 5 fps in our machine, while our tracker runs at 50 fps”
From Transformer Tracking (2021) · cites this paper as [39]
The cited source says what the claim attributes to it.
In the citing paper
“This table shows that the proposed method obtains the best performance, better than other trackers by a significant margin except SiamRCNN [39]”
From Transformer Tracking (2021) · cites this paper as [39]
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
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
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
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
(2016) · Cited by 31,590
Feature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939
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
Xception: Deep Learning with Depthwise Separable Convolutions
Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_49 · Cited by 20,054
Research works printed but not linked: 3
- wu, 2016, Tensorpack
- 10.1007/978-3-030-01261-8_1 · DOI · Paper page
- he, 0, Rethinking imagenet pretraining, ICCV
Cited by
Sorted by citations
Chen, 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
“This table shows that the proposed method obtains the best performance, better than other trackers by a significant margin except SiamRCNN [39], but SiamR-CNN merely runs less than 5fps in our machine, while our tracker runs at 50 fps.”
MethodDetection 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
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
“The proposed tracker achieves state-of-the-art performance on multiple challenging short-term and long-term benchmarks, while running at real-time speed, being 6× faster than Siam R-CNN [54].”
BackgroundMethodResultInfluentialMixFormer: End-to-End Tracking with Iterative Mixed Attention
Cui, Y., Jiang, C., Wang, L., & Wu, G. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01324 · Cited by 933
SeqTrack: Sequence to Sequence Learning for Visual Object Tracking
Chen, X., Peng, H., Wang, D., Lu, H., & Han, H. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01400 · Cited by 535
Transforming Model Prediction for Tracking
Mayer, C., Danelljan, M., Bhat, G., Paul, M., Paudel, D. P., Yu, F., & Gool, L. V. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00853 · Cited by 460
Learning Target Candidate Association to Keep Track of What Not to Track
Mayer, C., Danelljan, M., Paudel, D. P., & Gool, L. V. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01319 · Cited by 412
Transformer Tracking with Cyclic Shifting Window Attention
Song, Z., Yu, J., Chen, Y. P., & Yang, W. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00859 · Cited by 253
ODTrack: Online Dense Temporal Token Learning for Visual Tracking
Zheng, Y., Zhong, B., Liang, Q., Mo, Z., Zhang, S., & Li, X. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i7.28591 · Cited by 248
Learning What to Learn for Video Object Segmentation
Bhat, G., Lawin, F. J., Danelljan, M., Robinson, A., Felsberg, M., Van Gool, L., & Timofte, R. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58536-5_46 · Cited by 160
Universal Instance Perception as Object Discovery and Retrieval
Yan, B., Jiang, Y., Wu, J., Wang, D., Luo, P., Yuan, Z., & Lu, H. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01471 · Cited by 174
Compact Transformer Tracker with Correlative Masked Modeling
Song, Z., Luo, R., Yu, J., Chen, Y. P., & Yang, W. H. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i2.25327 · Cited by 112
Joint Inductive and Transductive Learning for Video Object Segmentation
Mao, Y., Wang, N., Zhou, W., & Li, H. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00953 · Cited by 108
Fang, Z., Zhou, S., Cui, Y., & Scherer, S. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3033034 · Cited by 93
RPT: Learning Point Set Representation for Siamese Visual Tracking
Ma, Z., Wang, L., Zhang, H., Lu, W. B., & Yin, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-68238-5_43 · Cited by 84
Xiao, B., Lin, Q., & Chen, Y. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103721 · Cited by 66
Learning residue-aware correlation filters and refining scale for real-time UAV tracking
Li, S., Liu, Y., Zhao, Q., & Feng, Z. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108614 · Cited by 47
A Unified Transformer-based Tracker for Anti-UAV Tracking
Yu, Q., Ma, Y., He, J., Yang, D., & Zhang, T. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00305 · Cited by 42
Deep-SAGA: a deep-learning-based system for automatic gaze annotation from eye-tracking data
Deane, O., Toth, E., & Yeo, S.-H. (2022) · Behavior Research Methods · doi:10.3758/s13428-022-01833-4 · Cited by 39
Siamese networks with distractor-reduction method for long-term visual object tracking
Xuan, S., Li, S., Zhao, Z., Kou, L., Zhou, Z., & Xia, G.-S. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107698 · Cited by 39
Adaptively bypassing vision transformer blocks for efficient visual tracking
Yang, X., Zeng, D., Wang, X., Wu, Y., Ye, H., Zhao, Q., & Li, S. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111278 · Cited by 39
Siamese visual tracking based on criss-cross attention and improved head network
Zhang, J., Huang, H., Jin, X., Kuang, L., & Zhang, J. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15429-3 · Cited by 37
Robust Long-Term Object Tracking via Improved Discriminative Model Prediction
Choi, S., Lee, J., Lee, Y., & Hauptmann, A. G. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-68238-5_40 · Cited by 29
Robust visual tracking with extreme point graph-guided annotation: Approach and experiment
Gao, P., Zhang, X., Yang, X., Gao, F., Fujita, H., & Wang, F. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122013 · Cited by 30
Guide Local Feature Matching by Overlap Estimation
Chen, Y., Huang, D., Xu, S., Liu, J., & Liu, Y. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i1.19913 · Cited by 26
Two-stage aware attentional Siamese network for visual tracking
Sun, X., Han, G., Guo, L., Yang, H., Wu, X., & Li, Q. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108502 · Cited by 27
Combining complementary trackers for enhanced long-term visual object tracking
Dunnhofer, M., Simonato, K., & Micheloni, C. (2022) · Image and Vision Computing · doi:10.1016/j.imavis.2022.104448 · Cited by 24
Lightweight full-convolutional siamese tracker
Li, Y., Wang, B., Wu, X., Liu, Z., & Li, Y. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111439 · Cited by 27
Spatio-temporal hierarchical feature transformer for UAV object tracking
Zhu, F., Cui, J., & Dou, K. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2023.09.016 · Cited by 21
BoLTVOS: Box-Level Tracking for Video Object SegmentationPreprint
Voigtlaender, P., Luiten, J., & Leibe, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.04552 · Cited by 28
Self-citationA review of current studies on the unmanned aerial vehicle-based moving target tracking methods
Yan, B., Wei, Y., Liu, S., Huang, W. G., Feng, R., & Chen, X. (2025) · Defence Technology · doi:10.1016/j.dt.2025.01.013 · Cited by 15
Optimizing intrinsic representation for tracking
Zhang, Y., & Sun, H. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111955 · Cited by 19
SiamCA: Siamese visual tracking with customized anchor and target-aware interaction
Pan, S., Zhang, C., Li, Z., & Hu, L. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121763 · Cited by 15
Sequential Fusion Based Multi-Granularity Consistency for Space-Time Transformer Tracking
Hu, K., Yang, W., Huang, W., Zhou, X., Cao, M., Ren, J., & Tan, H. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i11.29145 · Cited by 14
An IoU-aware Siamese network for real-time visual tracking
Wei, B., Chen, H., Cao, S., Ding, Q., & Luo, H. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.01.041 · Cited by 13
Siamese Graph Attention Networks for robust visual object tracking
Lu, J., Li, S., Guo, W., Zhao, M., Yang, J., Liu, Y., & Zhou, Z. (2023) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2023.103634 · Cited by 14
Pseudo loss active learning for deep visual tracking
Cui, Z.-Y., Lu, N., & Wang, W. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108773 · Cited by 15
Enhancing discriminative appearance model for visual tracking
He, X., & Chen, C. Y. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119670 · Cited by 13
GAA: Ghost Adversarial Attack for Object Tracking
Lei, M., Song, H., Fan, J., Xiao, D., Ai, D., Gu, Y., & Yang, J. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3369403 · Cited by 13
Decoding split-frequency representation for cross-scale tracking
Zhang, Y., & Sun, H. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107587 · Cited by 11
Chen, S., Wang, L., Wang, Z., Yan, Y., Wang, D., & Zhu, S. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.03.031 · Cited by 11
Active Learning for Deep Visual TrackingPreprint
Yuan, D., Chang, X., Yang, Y., Liu, Q., Wang, D., & He, Z. (2022) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/z36jf · Cited by 15
Deep Learning for Single-object Tracking: A Survey
Zhou, J., Yao, Y., & Yang, R. (2022) · IEEE International Conference on Software Engineering and Artificial Intelligence (SEAI) · doi:10.1109/seai55746.2022.9832135 · Cited by 9
Su, S., Liu, J., Gao, L., & Song, J. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i5.28300 · Cited by 9
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
Motion Deep Association for spatio-temporal object tracking
Guo, H., Du, X., & Wang, W. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111787 · Cited by 8
Visual tracking using transformer with a combination of convolution and attention
Wang, Y., Yan, L., Feng, Z., Xia, Y., & Xiao, B. (2023) · Image and Vision Computing · doi:10.1016/j.imavis.2023.104760 · Cited by 9
Video object segmentation based on multi-level target models and feature integration
Gao, B., Zhao, Y., Zhang, F., Luo, B., & Yang, C. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.04.042 · Cited by 8
Surface deformation tracking in monocular laparoscopic video
Liu, Z., Gao, W., Zhu, J., Yu, Z., & Fu, Y. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102775 · Cited by 8
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
A review of object tracking based on deep learning
Zhao, G., Meng, F., Yang, C., Wei, H., Zhang, D., & Zheng, Z. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130988 · Cited by 7
AEVRNet: Adaptive exploration network with variance reduced optimization for visual tracking
Yang, Y., Xing, W., Wang, D., Zhang, S., Yu, Q., & Wang, L. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.03.118 · Cited by 7
Dynamic and reliable subtask tracker with general schatten p -norm regularization
Fan, B., Cong, Y., Tian, J., & Tang, Y. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108129 · Cited by 7
AFAT: Adaptive Failure-Aware Tracker for Robust Visual Object TrackingPreprint
Xu, T., Feng, Z., Wu, X., & Kittler, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.13708 · Cited by 7
MSAF: Multi-scale adaptive filter for object tracking
Li, S., Huang, K., Chu, J., & Leng, L. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128715 · Cited by 7
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
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
Exploiting Rank-Based Filter Pruning for Real-Time Uav Tracking
Wang, X., Zeng, D., Zhao, Q., & Li, S. (2022) · Signal Processing Image Communication · doi:10.1016/j.image.2025.117278 · Cited by 6
Yan, Z., Ji, J., Ma, J., & Cao, W. (2024) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ad2a96 · Cited by 5
Visual Privacy Protection via Mapping Distortion
Li, Y., Liu, P., Jiang, Y., & Xia, S. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414149 · Cited by 7
Natural Language Guided Attention Mixer for Object Tracking
Lu, Q., Yuan, G., Li, C., Zhu, H., & Qin, X. (2023) · International Conference on Information Science, Parallel and Distributed Systems (ISPDS) · doi:10.1109/ispds58840.2023.10235726 · Cited by 5
Unambiguous Object Tracking by Exploiting Target Cues
Gao, J., Zhong, B., & Chen, Y. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612240 · Cited by 5
More robust object tracking via shape and motion cue integration
Gao, B., & Spratling, M. (2022) · Signal Processing · doi:10.1016/j.sigpro.2022.108628 · Cited by 5
DA-SACOT: Domain adaptive-segmentation guided attention for correlation based object tracking
Raju, P. M., Mishra, D., & Mukherjee, P. (2021) · Image and Vision Computing · doi:10.1016/j.imavis.2021.104215 · Cited by 5
An improved dynamic programming tracking-before-detection algorithm based on LSTM network
Song, F., Li, Y., Cheng, W., & Dong, L. (2023) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-023-01020-3 · Cited by 4
A Review on Techniques and Applications of Object Tracking and Gesture Recognition
Aggarwal, D., Banerjee, K., Bali, V., Sharma, R., Singh, S. K., Chaudhary, V., & Bhaskar, V. (2022) · International Mobile and Embedded Technology Conference (MECON) · doi:10.1109/mecon53876.2022.9751803 · Cited by 4
Zhong, P., Zeng, D., Wang, X., & Li, S. (2022) · Frontiers in artificial intelligence and applications · doi:10.3233/faia220392 · Cited by 4
Aortic Dissection Diagnosis Based on Sequence Information and燚eep燣earning
Peng, H., Tan, Y., Tang, H., Tan, L., Xiang, X., Wang, Y., & Xiong, N. N. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.029727 · Cited by 3
Edge-aware object pixel-level representation tracking
Jing, P., Huang, Z., Liu, J., Wang, Y., & Yu, J. (2022) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2022.103735 · Cited by 3
PointSiamRCNN: Target-aware Voxel-based Siamese Tracker for Point Clouds
Zou, H., Zhang, C., Liu, Y., Li, W., Wen, F., & Zhang, H. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636863 · Cited by 3
Improved Scale Adaptive KCF-based Method for Forward Looking Sonar Image
Ye, X., Luo, W., Huang, H., Xing, H., & Zhang, W. (2021) · MTS/IEEE OCEANS Conference (OCEANS) · doi:10.23919/oceans44145.2021.9706069 · 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
MINet: Modality interaction network for unified multi-modal tracking
Gong, S., Teng, Z., Li, R., Fan, J., Zhang, B., & Jianping Fan, B. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105071 · Cited by 2
Three-stage cascade architecture-based siamese sliding window network algorithm for object tracking
Yang, Z., Liu, K., Li, Q., Hou, Y., & Yan, Z. (2025) · Heliyon · doi:10.1016/j.heliyon.2024.e41612 · Cited by 2
AUNet: Adaptive UpdateNet for Dynamic Pedestrian Tracking with Short-Term Occlusion
Shen, L., Guo, H., Bai, Y., Ang, M. H., & Rus, D. L. (2021) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc48978.2021.9564802 · 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
Siamese network with a depthwise over-parameterized convolutional layer for visual tracking
Wang, Y., Zhang, W., Zhang, L., & Wang, J. (2022) · PLoS ONE · doi:10.1371/journal.pone.0273690 · Cited by 2
Multi-level Cross-attention Siamese Network For Visual Object Tracking
Zhang, J., Wang, J., Zhang, H., Miao, M., Cai, Z., & Chen, F. (2022) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2022.12.011 · Cited by 2
SMSTracker: A Self-Calibration Multi-Head Self-Attention Transformer for Visual Object Tracking
Wang, Z., Zhu, H., & Liu, F. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.050959 · Cited by 2
MFCFSiam: A Correlation-Filter-Guided Siamese Network with Multifeature for Visual Tracking
Li, C., Xing, Q., Ma, Z., & Zang, K. (2020) · Wireless Communications and Mobile Computing · doi:10.1155/2020/6681391 · Cited by 2
PatchNet -- Short-range Template Matching for Efficient Video ProcessingPreprint
Mao, H., Zhu, S., Han, S., & Dally, W. J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.07371 · Cited by 9
Accurate scale estimation for visual tracking with significant deformation
Chu, L., Li, H., & Yang, Z. (2020) · IET Computer Vision · doi:10.1049/iet-cvi.2019.0860 · Cited by 1
Video object segmentation based on dynamic perception update and feature fusion
Hou, Z., Li, F., Dong, J., Dai, N., Ma, S., & Fan, J. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105156 · Cited by 1
Binding double-head predictor with key context transformer for high performance tracking
Wang, B., Yu, H., Wan, W., Duan, Y., Gong, H., Jin, D., Xiu, Z., Song, Y. F., Wang, H., & Liu, G. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129444 · Cited by 1
SiamTP: A Transformer tracker based on target perception
Ren, Y., Wang, Z., Jing, Y., Chen, H., Yuan, L., Tian, H., & Wang, X. (2025) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2025.104426 · Cited by 1
SAVETrack: Scene-Adaptive Visual Enhancement Transformer for robust tracking
Wang, Y., & Hao, X. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130118 · Cited by 1
Research on gesture recognition algorithm based on target detection
Yan, X., Liu, X., Dong, L., Dong, F., & He, X. (2024) · doi:10.1145/3703935.3704048 · Cited by 1
Robust unsupervised visual tracking via image-to-video identity knowledge transferring
Kang, B., Wang, Z., Liang, D., Ding, T., & Ду, С. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112109 · Cited by 1
Cui, J., Wu, J., & Zhao, L. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.1080521 · Cited by 1
Siamese Tracker with Multi-Stage Unified Backbone Network
Zheng, H., Jinfen, Liu, & Gao, Y. (2023) · International Conference on Intelligent Computing and Signal Processing (ICSP) · doi:10.1109/icsp58490.2023.10248907 · Cited by 1
A Multiple Object Tracker with Spatiotemporal Memory Network
Peng, X., Chi, J., Wang, Z., Yan, F., & Liu, J. (2023) · International Journal of Aerospace Engineering · doi:10.1155/2023/9959178 · Cited by 1
Robust Probabilistic Discriminative Model Prediction Tracker via Improved Model Update StrategyPreprint
Chen, S., Qiu, C., Huang, Y., & Zhang, Z. (2021) · Research Square · doi:10.21203/rs.3.rs-1138271/v1 · Cited by 1
Xiao-yan, W. (2022) · Advances in Multimedia · doi:10.1155/2022/5785108
Rank-Based Filter Pruning for Real-Time Deep Uav TrackersPreprint
Wang, X., Zeng, D., Zhao, Q., & Li, S. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4159540
OS-DS Tracker: Orientation-Variant Siamese 3D Tracking with Detection Based Sampling 1Preprint
Zhang, Q., Wang, L., Li, W., Meng, H., Zhang, W., Zhang, Z., & Wang, P. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4174862
SiamDLA: Dynamic Label Assignment for Siamese Visual Tracking
Cai, Y., Tan, K., & Wei, Z. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.036177
Object detection with deep neural networks under constrained scenarios
Zhang, G. (2022) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/164687
Object Tracking Algorithm Based on Multi-Time-Space Perception and Instance-Specific Proposals
Sun, J., Li, D., & Cheng, H. (2023) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2023.038016
Qilong, 杨. Y., Xiaoyu, 马. M., Shuang, 刘. L., Shuanghui, 游. Y., & Chengping, 李. L. (2023) · Laser & Optoelectronics Progress · doi:10.3788/lop221228
Visual Tracking with Temporal Contextual Attention
Li, Q., & Zou, W. (2023) · International Conference on Computer Vision, Image and Deep Learning (CVIDL) · doi:10.1109/cvidl58838.2023.10166053
Metadata
- Authors
- Voigtlaender, P., Luiten, J., Torr, P. H. S., & Leibe, B.
- Affiliations
- RWTH Aachen University (DE)
- University of Oxford (GB)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 6577-6587
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2990431089
- Cited by
- 115
- References
- 114
- Citations by year
- 2026: 7 · 2025: 16 · 2024: 18 · 2023: 25 · 2022: 26 · 2021: 13 · 2020: 9
- FWCI
- 5.63
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- RWTH Publications (RWTH Aachen) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Multidisciplinary University Research Initiative (EP/N019474/1)
- Engineering and Physical Sciences Research Council (EP/N019474/1)
- H2020 European Research Council (773161)
- 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
- visual object tracking, two-stage object detection, hard example mining, long-term tracking
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
