The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Ensemble Tracking
Abstract
We consider tracking as a binary classification problem, where an ensemble of weak classifiers is trained online to distinguish between the object and the background. The ensemble of weak classifiers is combined into a strong classifier using AdaBoost. The strong classifier is then used to label pixels in the next frame as either belonging to the object or the background, giving a confidence map. The peak of the map and, hence, the new position of the object, is found using mean shift. Temporal coherence is maintained by updating the ensemble with new weak classifiers that are trained online during tracking. We show a realization of this method and demonstrate it on several video sequences.
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Cites
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884
CONDENSATION—Conditional Density Propagation for Visual Tracking
Isard, M., & Blake, A. (1998) · International Journal of Computer Vision · doi:10.1023/a:1008078328650 · Cited by 4,886
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
Cited by
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
Kalal, Z., Mikolajczyk, K., & Matas, J. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.239 · Cited by 3,285
MEEM: Robust Tracking via Multiple Experts Using Entropy Minimization
Zhang, J., Ma, S., & Sclaroff, S. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10599-4_13 · Cited by 1,117
Robust object tracking via sparsity-based collaborative model
Zhong, W., Lu, H., & Yang, M.-H. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247882 · Cited by 990
Metadata
- Authors
- Avidan, S.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2007-02
- Volume
- 29
- Issue
- 2
- Pages
- 261-271
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2293873019
- Cited by
- 1,112
- References
- 5
- FWCI
- 66.77
- Type
- journal article
- Language
- en
- Open access
- closed
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