The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Probabilistic tracking in joint feature-spatial spaces
Abstract
In this paper, we present a probabilistic framework for tracking regions based on their appearance. We exploit the feature-spatial distribution of a region representing an object as a probabilistic constraint to track that region over time. The tracking is achieved by maximizing a similarity-based objective function over transformation space given a nonparametric representation of the joint feature-spatial distribution. Such a representation imposes a probabilistic constraint on the region feature distribution coupled with the region structure, which yields an appearance tracker that is robust to small local deformations and partial occlusion. We present the approach for the general form of joint feature-spatial distributions and apply it to tracking with different types of image features including row intensity, color and image gradient.
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Citations
Cites
Efficient region tracking with parametric models of geometry and illumination
Hager, G. D., & Belhumeur, P. N. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.722606 · Cited by 1,066
Cited by
Real-Time Tracking via On-line Boosting
Grabner, H., Grabner, M., & Bischof, H. (2006) · Procedings of the British Machine Vision Conference 2006 · doi:10.5244/c.20.6 · Cited by 1,198
Online selection of discriminative tracking features
Collins, R. T., Liu, Y., & Leordeanu, M. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.205 · Cited by 1,208
Deep Metric Learning: A Survey
KAYA, M., & BİLGE, H. Ş. (2019) · Symmetry · doi:10.3390/sym11091066 · Cited by 703
Metadata
- Authors
- Elgammal, A., Duraiswami, R., & Davis, L. S.
- Venue
- 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings.
- Publisher
- IEEE Comput. Soc
- Published
- 2003-11-04
- Pages
- I-781-I-788
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2117239879
- Cited by
- 170
- References
- 1
- FWCI
- 6.17
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Elgammal, A., Duraiswami, R., & Davis, L. S. (2003). Probabilistic tracking in joint feature-spatial spaces. 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. https://doi.org/10.1109/cvpr.2003.1211432
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