The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Beyond semi-supervised tracking: Tracking should be as simple as detection, but not simpler than recognition
Abstract
We present a multiple classifier system for model-free tracking. The tasks of detection (finding the object of interest), recognition (distinguishing similar objects in a scene), and tracking (retrieving the object to be tracked) are split into separate classifiers in the spirit of simplifying each classification task. The supervised and semi-supervised classifiers are carefully trained on-line in order to increase adaptivity while limiting accumulation of errors, i.e. drifting. In the experiments, we demonstrate real-time tracking on several challenging sequences, including multi-object tracking of faces, humans, and other objects. We outperform other on-line tracking methods especially in case of occlusions and presence of similar objects.
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Cites
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
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
Competitive learning: From interactive activation to adaptive resonance
Grossberg, S. (1987) · Cognitive Science · doi:10.1111/j.1551-6708.1987.tb00862.x · Cited by 1,471
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
Robust object tracking with online multiple instance learning
Babenko, B., Yang, M.-H., & Belongie, S. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.226 · Cited by 2,007
Encoding Color Information for Visual Tracking: Algorithms and Benchmark
Liang, P., Blasch, E., & Ling, H. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2482905 · Cited by 726
Metadata
- Authors
- Stalder, S., Grabner, H., & van Gool, L.
- Venue
- 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops
- Publisher
- IEEE
- Published
- 2009-09
- Pages
- 1409-1416
- OpenAlex
- W2034276366
- Cited by
- 214
- References
- 3
- FWCI
- 13.09
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Stalder, S., Grabner, H., & van Gool, L. (2009). Beyond semi-supervised tracking: Tracking should be as simple as detection, but not simpler than recognition. 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops. https://doi.org/10.1109/iccvw.2009.5457445
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