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.
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On GOT-10k, TransT-GOT performs 2.2% higher than SiamR-CNN in the main AO metric.
“TransT-GOT method performs 2.2% higher than SiamR-CNN in the main AO metric.”
From Transformer Tracking (2021) · cites this paper as [39]
The GOT-10k results in the NfS section report a 64.9% success rate for Siam R-CNN. Our copy does not report a TransT-GOT result or a 2.2-percentage-point AO comparison with it, so the claimed difference is unsupported. (LLM, conf=0.82)
Experimental — accuracy not yet measuredSiamR-CNN runs at less than 5 fps on the authors' machine, while TransT runs at 50 fps.
“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]
On the LaSOT dataset, TransT obtains the best performance among compared trackers, better than other trackers by a significant margin except SiamR-CNN.
“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]
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Learning Spatial-Aware Regressions for Visual Tracking
Sun, C., Wang, D., Lu, H., & Yang, M. (2018) · 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/cvpr.2018.00934 · Cited by 226 · see citing works
‘Skimming-Perusal’ Tracking: A Framework for Real-Time and Robust Long-Term Tracking
Yan, B., Zhao, H., Wang, D., Lu, H., & Yang, X. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00247 · Cited by 218 · see citing works
Video Object Segmentation with Joint Re-identification and Attention-Aware Mask Propagation
Li, X., & Loy, C. C. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01219-9_6 · Cited by 211 · see citing works
CNN in MRF: Video Object Segmentation via Inference in a CNN-Based Higher-Order Spatio-Temporal MRF
Bao, L., Wu, B., & Liu, W. (2018) · 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/cvpr.2018.00626 · Cited by 207 · see citing works
Meta-tracker: Fast and Robust Online Adaptation for Visual Object Trackers
Park, E., & Berg, A. C. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01219-9_35 · Cited by 189 · see citing works
Correlation Tracking via Joint Discrimination and Reliability Learning
Sun, C., Wang, D., Lu, H., & Yang, M. (2018) · 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/cvpr.2018.00058 · Cited by 185 · see citing works
Hyperparameter Optimization for Tracking with Continuous Deep Q-Learning
Dong, X., Shen, J., Wang, W., Liu, Y., Shao, L., & Porikli, F. (2018) · 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/cvpr.2018.00061 · Cited by 168 · see citing works
Long-Term Tracking in the Wild: A Benchmark
Valmadre, J., Bertinetto, L., Henriques, J. F., Ran, T., Vedaldi, A., Smeulders, A. W. M., Torr, P. H. S., & Gavves, E. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01219-9_41 · Cited by 157 · see citing works
Self-citationReal-Time ‘Actor-Critic’ Tracking
Chen, B., Wang, D., Li, P., Wang, S., & Lu, H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01234-2_20 · Cited by 141 · see citing works
SINT++: Robust Visual Tracking via Adversarial Positive Instance Generation
Wang, X., Li, C., Luo, B., & Tang, J. (2018) · 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/cvpr.2018.00511 · Cited by 123 · see citing works
Deep Meta Learning for Real-Time Target-Aware Visual Tracking
Choi, J., Kwon, J., & Lee, K. M. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00100 · Cited by 117 · see citing works
High-Speed Tracking with Multi-kernel Correlation Filters
Tang, M., Yu, B., Zhang, F., & Wang, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00512 · Cited by 103 · see citing works
ROI Pooled Correlation Filters for Visual Tracking
Sun, Y., Sun, C., Wang, D., He, Y., & Lu, H. (2019) · 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/cvpr.2019.00593 · Cited by 101 · see citing works
Learning Feature Embeddings for Discriminant Model Based Tracking
Zheng, L., Tang, M., Chen, Y., Wang, J., & Lu, H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58555-6_45 · Cited by 96 · see citing works
Saffari, A., Godec, M., Pock, T., Leistner, C., & Bischof, H. (2010) · 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/cvpr.2010.5539937 · Cited by 91 · see citing works
Superpixel-Based Tracking-by-Segmentation Using Markov Chains
Yeo, D., Son, J., Han, B., & Han, J. H. (2017) · 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/cvpr.2017.62 · Cited by 83 · see citing works
Learning regression and verification networks for long-term visual trackingPreprint
Zhang, Y., Wang, D., Wang, L., Qi, J., & Lu, H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.04320 · Cited by 64 · see citing works
Deep Reinforcement Learning with Iterative Shift for Visual Tracking
Ren, L., Yuan, X., Lu, J., Yang, M., & Zhou, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01240-3_42 · Cited by 62 · see citing works
Visual Tracking via Spatially Aligned Correlation Filters Network
Zhang, M., Wang, Q., Xing, J., Gao, J. G., Peng, P., Hu, W., & Maybank, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01219-9_29 · Cited by 58 · see citing works
Bridging the Gap Between Detection and Tracking: A Unified Approach
Huang, L., Zhao, X., & Huang, K. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00410 · Cited by 54 · see citing works
Now you see me: evaluating performance in long-term visual tracking
Lukežič, A., Zajc, L. Č., Vojíř, T., Matas, J., & Kristan, M. (2018) · Cambridge University Engineering Department Publications Database · doi:10.48550/arxiv.1804.07056 · Cited by 50 · see citing works
Joint Representation and Truncated Inference Learning for Correlation Filter Based Tracking
Yao, Y., Wu, X., Lei Zhang, A., Shan, S., & Zuo, W. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01240-3_34 · Cited by 48 · see citing works
Tracking Holistic Object RepresentationsPreprint
Sauer, A., Aljalbout, E., & Haddadin, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.12920 · Cited by 36 · see citing works
Tracking the Known and the Unknown by Leveraging Semantic Information.
Tripathi, A. S., Danelljan, M., Gool, L. V., & Timofte, R. (2019) · Lirias · Cited by 14 · see citing works
Learning Feature Embeddings for Discriminant Model based TrackingPreprint
Zheng, L., Tang, M., Chen, Y., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.10414 · Cited by 6 · see citing works
Learning Features with Differentiable Closed-Form Solver for TrackingPreprint
Zheng, L., Tang, M., JinqiaoWang, & Lu, H. (2019) · arXiv (Cornell University) · Cited by 5 · see citing works
Flagged first, then by citations
Research works printed but not linked: 5
- K. He, G. Gkioxari, P. Dollár, and R. Girshick. Mask R- CNN. In ICCV, 2017. 2
- L. Zhang, A. Gonzalez-Garcia, J. van de Weijer, M. Danell- jan, and F. S. Khan. Learning the model update for siamese trackers. In ICCV, 2019. 17, 20
- Y. Zhang, L. Wang, J. Qi, D. Wang, M. Feng, and H. Lu. Structured siamese network for real-time visual tracking. In ECCV, 2018. 20
- L. Zheng, M. Tang, Y. Chen, J. Wang, and H. Lu. Fast- deepkcf without boundary effect. In ICCV, 2019. 20
- Z. Zhu, W. Wu, W. Zou, and J. Yan. End-to-end flow cor- relation tracking with spatial-temporal attention. In CVPR, 2018. 17, 20 No hard ex. min. -1.7 -3.0 -1.3 -2.2 -2.2 -2.3 -5.3 -2.1 -1.7 -2.1 -1.2 -1.7 No TDPA (Argmax) -6.3 -7.9 -1.5 -5.6 -9.5 -3.3 -14.0 -5.8 -4.6 -8.4 -6.8 -7.2 No TDPA (Short-term) -2.8 -6.5 -2.8 -3.5 -1.6 -2.5 -6.2 -8.4 -4.8 -3.7 -14.5 -5.3 0 RoIs -12.9 -14.0 -10.5 -20.5 -10.5 -12.2 -20.3 -25.8 -14.9 -12.1 -21.8 -13.8 100 RoIs -1.4 -1.8 +1.2 -3.6 -3.1 -0.8 -7.8 -4.5 -1.6 -2.2 -4.2 -2.1 10000 RoIs -0.5 -0.8 0.0 -0.1 -0.6 +0.4 +0.8 0.0 -0.7 -0.7 -0.3 -0.6 DiMP-50 [5] -1.3 -5.3 +3.7 -2.6 -1.4 -2.2 -8.1 -4.3 -0.2 -0.8 -5.4 -2.8 SiamRPN++ [] -0.4 -0.0 +1.7 -2.0 -0.5 -0.3 -5.1 -3.0 -0.3 -0.3 -3.1 -2.4
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 · see citing works
“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 · see citing works
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 · see citing works
“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 890 · see citing works
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 501 · see citing works
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 · see citing works
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 392 · see citing works
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 · see citing works
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 223 · see citing works
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 · see citing works
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 152 · see citing works
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 · see citing works
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 104 · see citing works
Fang, Z., Zhou, S., Cui, Y., & Scherer, S. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3033034 · Cited by 92 · see citing works
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 77 · see citing works
Xiao, B., Lin, Q., & Chen, Y. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103721 · Cited by 66 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 29 · see citing works
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 · see citing works
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 25 · see citing works
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 · see citing works
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 23 · see citing works
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 · see citing works
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 27 · see citing works
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 · see citing works
Optimizing intrinsic representation for tracking
Zhang, Y., & Sun, H. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111955 · Cited by 17 · see citing works
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 14 · see citing works
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 · see citing works
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 · see citing works
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 13 · see citing works
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 · see citing works
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 · see citing works
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 12 · see citing works
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 · see citing works
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 · see citing works
Active Learning for Deep Visual TrackingPreprint
Yuan, D., Chang, X., Yang, Y., Liu, Q., Wang, D., & He, Z. (2022) · doi:10.31219/osf.io/z36jf · Cited by 14 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 8 · see citing works
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 · see citing works
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 · see citing works
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 7 · see citing works
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 6 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 6 · see citing works
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 6 · see citing works
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 · see citing works
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 · see citing works
Yan, Z., Ji, J., Ma, J., & Cao, W. (2024) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ad2a96 · Cited by 5 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Zhong, P., Zeng, D., Wang, X., & Li, S. (2022) · Frontiers in artificial intelligence and applications · doi:10.3233/faia220392 · Cited by 4 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Zhou, L., Jin, Y. J., Wang, H., Hu, Z., & Zhao, S. (2022) · Signal Processing · doi:10.1016/j.sigpro.2022.108463 · Cited by 2 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Cui, J., Wu, J., & Zhao, L. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.1080521 · Cited by 1 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 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
- 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 · 2019: 1
- 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
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