The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Support Vector Tracking
Abstract
Support Vector Tracking (SVT) integrates the Support Vector Machine (SVM) classifier into an optic-flow based tracker. Instead of minimizing an intensity difference function between successive frames, SVT maximizes the SVM classification score. To account for large motions between successive frames, we build pyramids from the support vectors and use a coarse-to-fine approach in the classification stage. We show results of using a homogeneous quadratic polynomial kernel-SVT for vehicle tracking in image sequences.
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Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Hierarchical Model-Based Motion Estimation
Anandan, P., Bergen, J. R., Hanna, K. J., & Hingorani, R. (1993) · Motion Analysis and Image Sequence Processing · doi:10.1007/978-1-4615-3236-1_1 · Cited by 193
Cited by
Comaniciu, D., Ramesh, V., & Meer, P. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1195991 · Cited by 4,560
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
Metadata
- Authors
- Avidan, S.
- Venue
- Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001
- Publisher
- IEEE Comput. Soc
- Published
- 2005-08-25
- Volume
- 1
- Pages
- I-184-I-191
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2152668169
- Cited by
- 170
- References
- 2
- FWCI
- 10.68
- Type
- conference paper
- Language
- en
- Open access
- closed
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