The DOI matches its Crossref record; 87 of the 88 references it cites were found in a registry or an index.
Kernel-based object tracking
Abstract
A new approach toward target representation and localization, the central component in visual tracking of nonrigid objects, is proposed. The feature histogram-based target representations are regularized by spatial masking with an isotropic kernel. The masking induces spatially-smooth similarity functions suitable for gradient-based optimization, hence, the target localization problem can be formulated using the basin of attraction of the local maxima. We employ a metric derived from the Bhattacharyya coefficient as similarity measure, and use the mean shift procedure to perform the optimization. In the presented tracking examples, the new method successfully coped with camera motion, partial occlusions, clutter, and target scale variations. Integration with motion filters and data association techniques is also discussed. We describe only a few of the potential applications: exploitation of background information, Kalman tracking using motion models, and face tracking.
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“Although object tracking has been studied for several decades, and much progress has been made in recent years [28, 16, 47, 5, 40, 26, 19], it remains a very challenging problem.”
From Object Tracking Benchmark (2015) · cites this paper as [16]
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A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
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Mean shift: a robust approach toward feature space analysis
Comaniciu, D., & Meer, P. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.1000236 · Cited by 11,523
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1 of the 88 references couldn’t be matched to a known paper.
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Yılmaz, A., Javed, O., & Shah, M. A. (2006) · ACM Computing Surveys · doi:10.1145/1177352.1177355 · Cited by 4,902
Online 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
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
Temporal Fusion Transformers for interpretable multi-horizon time series forecasting
Lim, B., Arık, S. Ö., Loeff, N., & Pfister, T. (2021) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2021.03.012 · Cited by 3,291
Kalal, Z., Mikolajczyk, K., & Matas, J. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.239 · Cited by 3,332
Metadata
- Authors
- Comaniciu, D., Ramesh, V., & Meer, P.
- Affiliations
- Siemens (United States) (US)
- Institute of Electrical and Electronics Engineers (US)
- Rutgers, The State University of New Jersey (US)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2003-05
- Volume
- 25
- Issue
- 5
- Pages
- 564-577
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2132103241
- Cited by
- 4,560
- References
- 88
- Citations by year
- 2026: 14 · 2025: 23 · 2024: 47 · 2023: 46 · 2022: 51 · 2021: 96 · 2020: 108 · 2019: 150 · 2018: 181 · 2017: 224 · 2016: 270 · 2015: 336 · 2014: 397 · 2013: 363 · 2012: 379
- FWCI
- 134.18
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (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 Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Infrared Target Detection Methodologies
- Keywords
- object tracking, kernel-based regularization, feature histograms, spatial masking, isotropic kernels, Bhattacharyya coefficient, mean shift, target localization, camera movement, partial occlusion, target scale variation, data association, face tracking
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