Real-Time Scalable Visual Tracking via Quadrangle Kernelized Correlation Filters
Open accessAbstract
Correlation filter (CF) has been widely used in tracking tasks due to its simplicity and high efficiency. However, conventional CF-based trackers fail to handle the scale variation that occurs when the targeted object is moving, which is one of the most notable unsolved problems of visual object tracking. In this paper, we propose a scalable visual tracking algorithm based on kernelized correlation filters, referred to as quadrangle kernelized correlation filters (QKCF). Unlike existing complicated scalable trackers that either perform the correlation filtering operation multiple times or extract many candidate windows at various scales, our tracker intends to estimate the scale of the object based on the positions of its four corners, which can be detected using a new Gaussian training output matrix within one filtering process. After obtaining four peak values corresponding to the four corners, we measure the detection confidence of each part response by evaluating its spatial and temporal smoothness. On top of it, a weighted Bayesian inference framework is employed to estimate the final location and size of the bounding box from the response matrix, where the weights are synchronized with the calculated detection likelihoods. Experiments are performed on the OTB-100 data set and 16 benchmark sequences with significant scale variations. The results demonstrate the superiority of the proposed method in terms of both effectiveness and robustness, compared with the state-of-the-art methods.
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CCA is more effective than PCA in extracting discriminative information with robustness in anti-noise.
“In contrast to Principle Component Analysis (PCA), CCA is more effective in extracting discriminative information with the robustness in anti-noise [3], thus obtaining more discriminative video features after the projection.”
From Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval (2019) · cites this paper as [3]
Section III describes a kernelized correlation-filter tracker, and the Experimental Settings section says it uses HOG features. Searches for CCA, PCA, and their full names found no passage comparing their discriminative information or noise robustness. The copy therefore does not address the attributed claim. (LLM, conf=0.77)
Experimental — accuracy not yet measuredThe total variance remains unchanged with the original properties (e.g., global Euclidean or local manifold structure) preserved after orthogonal rotation.
“As observed from the above equation, the total variance remains unchanged with the original properties (e.g., global Euclidean or local manifold structure) preserved after the orthogonal rotation [3], [56].”
From Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval (2019) · cites this paper as [3]
The paper’s section on quadrangle Gaussian training labels discusses offsets used to locate target edges, while its weighted interpolation section discusses tracking-model updates. Neither passage addresses whether orthogonal rotation preserves total variance, Euclidean structure, or manifold structure; searches for those terms found no relevant passage in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThe optimization approach transforms hash function learning into a regression problem with binary code B as the regression target, avoiding a relaxation strategy for nonconvex binary constraints.
“More importantly, our optimization approach transforms the hash function learning into a regression problem with binary code B as the regression target, thus avoiding the use of a relaxation strategy to solve the nonconvex equation with binary constraints in most published works [3], [8], [18], [35], [48], [57], [58].”
From Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval (2019) · cites this paper as [3]
The cited paper’s Introduction and Weighted Interpolation Updating section describe a visual-tracking method, not hash-function learning. Searches for binary-code regression and relaxation of binary constraints found no passage addressing the point for which this paper is cited. (LLM, conf=0.76)
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Hu, R., Tang, Z., & Yang, R. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110149 · Cited by 7 · see citing works
Jiuxin, Z., & Zhou, P. (2018) · IEEE/ION Position Location and Navigation Symposium · doi:10.1109/plans.2018.8373408 · Cited by 6 · see citing works
Size-aware visual object tracking via dynamic fusion of correlation filter-based part regressors
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A polar model for fast object tracking in 360-degree camera images
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Multi-Level Ensemble Network for Scene Recognition
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Learning Dynamical Position Embedding for Discriminative Segmentation Tracking
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2018 IndexIEEE Transactions on Intelligent Transportation SystemsVol. 19
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Modality Correlation Aware Sparse Representation and Kiou Tracker for Rgb-infrared VideosPreprint
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- Authors
- Ding, G., Chen, W., Zhao, S., Han, J., & Liu, Q.
- Affiliations
- Tsinghua University (CN)
- Lancaster University (GB)
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2018-01
- Volume
- 19
- Issue
- 1
- Pages
- 140-150
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W2770189358
- Cited by
- 54
- References
- 60
- Citations by year
- 2026: 1 · 2024: 1 · 2023: 5 · 2022: 1 · 2021: 5 · 2020: 13 · 2019: 16 · 2018: 12
- FWCI
- 4.66
- Type
- journal article
- Language
- en
- Open access
- green
- eprints.lancs.ac.uk
- Locations
- IEEE Transactions on Intelligent Transportation Systems (publishedVersion)
- Lancaster EPrints (Lancaster University) (submittedVersion)
- Funders
- National Natural Science Foundation of China (61571269, 61671267, 61701273)
- China Post-Doctoral Science Foundation (2017M610897)
- Royal Society Newton Mobility (IE150997)
- 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 Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- visual object tracking, correlation filter, kernelized correlation filter, scale variation, detection confidence, bounding box estimation, real-time tracking
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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.
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