Exploiting the Circulant Structure of Tracking-by-Detection with Kernels
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Correlation filter-based visual object tracking has an online learning process that allows updating the template/model in every frame of a video.
“Another advantage of correlation filter-based visual object tracking is its online learning process, which allows updating the template/model in every frame of a video [1,2].”
From Multiple Cues-Based Robust Visual Object Tracking Method (2022) · cites this paper as [1]
Correlation filter-based tracking algorithms have shown superiority in terms of computational cost.
“These algorithms have shown superiority in terms of computational cost.”
From Multiple Cues-Based Robust Visual Object Tracking Method (2022) · cites this paper as [1]
Discriminative methods are superior in terms of accuracy and computational cost.
“As per the literature, discriminative methods are superior in terms of accuracy and computational cost. Some of the examples of these trackers are given in [1,10-12].”
From Multiple Cues-Based Robust Visual Object Tracking Method (2022) · cites this paper as [1]
The precision plot has been adopted to measure the overall tracking performance.
“Recently the precision plot [6, 27] has been adopted to measure the overall tracking performance.”
From Object Tracking Benchmark (2015) · cites this paper as [27]
Cites
Incremental Learning for Robust Visual Tracking
Ross, D. A., Lim, J., Lin, R.-S., & Yang, M.-H. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0075-7 · Cited by 3,061 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
High-Speed Tracking with Kernelized Correlation Filters
Henriques, J. F., Caseiro, R., Martins, P., & Batista, J. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2345390 · Cited by 5,753 · see citing works
Self-citationOnline Object Tracking: A Benchmark
Wu, Y., Lim, J., & Yang, M.-H. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.312 · Cited by 4,138 · see citing works
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 · see citing works
Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking
Danelljan, M., Robinson, A., Shahbaz Khan, F., & Felsberg, M. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46454-1_29 · Cited by 1,853 · see citing works
LaSOT: A High-Quality Benchmark for Large-Scale Single Object Tracking
Fan, H., Lin, L., Yang, F., Chu, P., Deng, G., Yu, S., Bai, H., Xu, Y., Liao, C., & Ling, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00552 · Cited by 1,742 · see citing works
“We evaluate 35 algorithms on LaSOT to provide extensive baselines, comprising deep trackers ( e.g ., MDNet [42], TRACA [5], CFNet [50], SiamFC [4], StructSiam [59], DSiam [16], SINT [49] and VITAL [48]), correlation fil-ter trackers with hand-crafted features ( e.g ., ECO HC [7], DSST [8], CN [11], CSK [19], KCF [20],…”
Discriminative Scale Space Tracking
Danelljan, M., Hager, G., Khan, F. S., & Felsberg, M. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2609928 · Cited by 1,315 · see citing works
Discriminative Correlation Filter with Channel and Spatial Reliability
Lukezic, A., Vojir, T., Zajc, L. C., Matas, J., & Kristan, M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.515 · Cited by 1,153 · see citing works
Encoding Color Information for Visual Tracking: Algorithms and Benchmark
Liang, P., Blasch, E., & Ling, H. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2482905 · Cited by 726 · see citing works
In defense of color-based model-free tracking
Possegger, H., Mauthner, T., & Bischof, H. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298823 · Cited by 370 · see citing works
LaSOT: A High-quality Large-scale Single Object Tracking Benchmark
Fan, H., Bai, H., Lin, L., Yang, F., Chu, P., Deng, G., Yu, S., Harshit, Huang, M., Liu, J., et al. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01387-y · Cited by 360 · see citing works
Overview and methods of correlation filter algorithms in object tracking
Liu, S., Liu, D., Srivastava, G., Połap, D., & Woźniak, M. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-020-00161-4 · Cited by 305 · see citing works
Adaptive Correlation Filters with Long-Term and Short-Term Memory for Object Tracking
Ma, C., Huang, J.-B., Yang, X., & Yang, M.-H. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-018-1076-4 · Cited by 156 · see citing works
Event Retrieval in Large Video Collections with Circulant Temporal Encoding
Revaud, J., Douze, M., Schmid, C., & Jegou, H. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.318 · Cited by 133 · see citing works
A Systematic Review of Drone Based Road Traffic Monitoring System
Bisio, I., Garibotto, C., Haleem, H., Lavagetto, F., & Sciarrone, A. (2022) · IEEE Access · doi:10.1109/access.2022.3207282 · Cited by 121 · see citing works
“Furthermore, concern-821 ing the data preparation task in [95], circulant structure of tracking-by-detection with kernels [117] was used to annotate the training images of a video scene, while rotation based data augmentation technique was used to increase the number of training samples.”
MethodHigh-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
Regional deep learning model for visual tracking
Wu, G., Lu, W., Gao, G., Zhao, C., & Liu, J. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.10.064 · Cited by 78 · see citing works
A survey on online learning for visual tracking
Abbass, M. Y., Kwon, K.-C., Kim, N., Abdelwahab, S. A., El-Samie, F. E. A., & Khalaf, A. A. M. (2021) · The Visual Computer · doi:10.1007/s00371-020-01848-y · Cited by 64 · see citing works
Unmanned Aerial Vehicle Video-Based Target Tracking Algorithm Using Sparse Representation
Wan, M., Gu, G., Qian, W., Ren, K., Maldague, X., & Chen, Q. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2930656 · Cited by 62 · see citing works
Real-Time Scalable Visual Tracking via Quadrangle Kernelized Correlation Filters
Ding, G., Chen, W., Zhao, S., Han, J., & Liu, Q. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2774778 · Cited by 54 · see citing works
Fast and Robust Object Tracking Using Tracking Failure Detection in Kernelized Correlation Filter
Shin, J., Kim, H., Kim, D., & Paik, J. (2020) · Applied Sciences · doi:10.3390/app10020713 · Cited by 42 · see citing works
SITUP: Scale Invariant Tracking Using Average Peak-to-Correlation Energy
Ma, H., Acton, S. T., & Lin, Z. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2962694 · Cited by 39 · see citing works
Efficient object tracking using hierarchical convolutional features model and correlation filters
Abbass, M. Y., Kwon, K.-C., Kim, N., Abdelwahab, S. A., El-Samie, F. E. A., & Khalaf, A. A. M. (2021) · The Visual Computer · doi:10.1007/s00371-020-01833-5 · Cited by 24 · see citing works
Motion-Aware Correlation Filters for Online Visual Tracking
Zhang, Y., Yang, Y., Zhou, W., Shi, L., & Li, D. (2018) · Sensors · doi:10.3390/s18113937 · Cited by 20 · see citing works
Improved kernelized correlation filter tracking by using spatial regularization
Gao, L., Li, Y., & Ning, J. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2017.11.008 · Cited by 14 · see citing works
Yang, X., Li, S., Yu, J., Zhang, K., Yang, J., & Yan, J. (2021) · Infrared Physics & Technology · doi:10.1016/j.infrared.2021.103958 · Cited by 14 · see citing works
On the improvement of foreground–background model‐based object tracker
Hanif, M. S., Ahmad, S., & Khurshid, K. (2017) · IET Computer Vision · doi:10.1049/iet-cvi.2016.0487 · Cited by 14 · see citing works
Multiple Cues-Based Robust Visual Object Tracking Method
Khan, B., Jalil, A., Ali, A., Alkhaledi, K., Mehmood, K., Cheema, K. M., Murad, M., Tariq, H., & El-Sherbeeny, A. M. (2022) · Electronics · doi:10.3390/electronics11030345 · Cited by 5 · see citing works
- Authors
- Henriques, J. F., Caseiro, R., Martins, P., & Batista, J.
- Affiliations
- University of Coimbra (PT)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2012
- Pages
- 702-715
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W161114242
- Cited by
- 2,310
- References
- 1
- Citations by year
- 2026: 23 · 2025: 76 · 2024: 107 · 2023: 135 · 2022: 212 · 2021: 229 · 2020: 251 · 2019: 327 · 2018: 312 · 2017: 240 · 2016: 196 · 2015: 137 · 2014: 54 · 2013: 10 · 2012: 1
- FWCI
- 45.89
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Lecture notes in computer science (publishedVersion)
- 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 › Advanced Image and Video Retrieval Techniques
- Keywords
- tracking-by-detection, circulant matrices, kernel methods, fast Fourier transform, online learning, real-time object tracking, Gaussian kernel, polynomial kernel, analytical solution, discriminative classifiers, Fourier analysis
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
