The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Overview and methods of correlation filter algorithms in object tracking
Open accessAbstract
Abstract An important area of computer vision is real-time object tracking, which is now widely used in intelligent transportation and smart industry technologies. Although the correlation filter object tracking methods have a good real-time tracking effect, it still faces many challenges such as scale variation, occlusion, and boundary effects. Many scholars have continuously improved existing methods for better efficiency and tracking performance in some aspects. To provide a comprehensive understanding of the background, key technologies and algorithms of single object tracking, this article focuses on the correlation filter-based object tracking algorithms. Specifically, the background and current advancement of the object tracking methodologies, as well as the presentation of the main datasets are introduced. All kinds of methods are summarized to present tracking results in various vision problems, and a visual tracking method based on reliability is observed.
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“By employing a correlation tracking algorithm, the system can track the object based the characteristics of its appearance [93].”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [93]
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Cites
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
Visual object tracking using adaptive correlation filters
Bolme, D., Beveridge, J. R., Draper, B. A., & Lui, Y. M. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539960 · Cited by 3,406
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×
“100 video sequences are contained in extended version OTB2015 [11] which is extended from the OTB2013 (50 videos).”
BackgroundExploiting the Circulant Structure of Tracking-by-Detection with Kernels
Henriques, J. F., Caseiro, R., Martins, P., & Batista, J. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33765-9_50 · Cited by 2,310
Learning Spatially Regularized Correlation Filters for Visual Tracking
Danelljan, M., Hager, G., Khan, F. S., & Felsberg, M. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.490 · Cited by 2,180
Accurate Scale Estimation for Robust Visual Tracking
Danelljan, M., Hager, G., Khan, F. S., & Felsberg, M. (2014) · British Machine Vision Conference (BMVC) · doi:10.5244/c.28.65 · Cited by 2,145
A Benchmark and Simulator for UAV Tracking
Mueller, M., Smith, N., & Ghanem, B. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_27 · Cited by 1,944
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
Staple: Complementary Learners for Real-Time Tracking
Bertinetto, L., Valmadre, J., Golodetz, S., Mikšík, O., & Torr, P. H. S. (2016) · 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.2016.156 · Cited by 1,825
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 · cited 1×
“In addition to these basic data sets, the latest LASOT [14] data set consists Fig.”
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Sapkota, R., Flores-Calero, M., Qureshi, R., Badjugar, C., Nepal, U., Poulose, A., Zeno, P. J., Vaddevolu, U. B. P., Khan, S., Shoman, M., et al. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11253-3 · Cited by 528
Plant Disease Recognition Model Based on Improved YOLOv5
Chen, Z., Wu, R., Lin, Y., Li, C., Chen, S., Yuan, Z., Chen, S., & Zou, X. (2022) · Agronomy · doi:10.3390/agronomy12020365 · Cited by 274
A Survey of Video Surveillance Systems in Smart City
Myagmar-Ochir, Y., & Kim, W. (2023) · Electronics · doi:10.3390/electronics12173567 · Cited by 134
Fu, C., Li, B., Ding, F., Lin, F., & Lu, G. (2021) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2021.3072992 · Cited by 98
Hand gesture recognition based on a Harris Hawks optimized Convolution Neural Network
Gadekallu, T. R., Srivastava, G., Liyanage, M., Iyapparaja, M., Chowdhary, C. L., Koppu, S., & Maddikunta, P. K. R. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.107836 · Cited by 78
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Metadata
- Authors
- Liu, S., Liu, D., Srivastava, G., Połap, D., & Woźniak, M.
- Affiliations
- Hunan Normal University (CN)
- Inner Mongolia University (CN)
- China Medical University (TW)
- Silesian University of Technology (PL)
- Corresponding author
- Srivastava, Gautam
- Venue
- Complex & Intelligent Systems
- Published
- 2020-06-09
- Volume
- 7
- Issue
- 4
- Pages
- 1895-1917
- ISSN
- 2198-6053 (ISSN-L)
- 2199-4536
- OpenAlex
- W3034326629
- Cited by
- 305
- References
- 37
- Citations by year
- 2026: 5 · 2025: 19 · 2024: 27 · 2023: 36 · 2022: 83 · 2021: 106 · 2020: 26
- FWCI
- 24.40
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Complex & Intelligent Systems (publishedVersion, cc-by)
- Funders
- Zhejiang University
- State Key Laboratory of Computer Aided Design and Computer Graphics
- Science and Technology Program of Hunan Province
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- correlation filter, single object tracking, real-time object tracking, scale variation, occlusion, edge effects, tracking dataset
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