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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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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
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 ›
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