The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Spatiotemporal salient points for visual recognition of human actions
Abstract
This paper addresses the problem of human-action recognition by introducing a sparse representation of image sequences as a collection of spatiotemporal events that are localized at points that are salient both in space and time. The spatiotemporal salient points are detected by measuring the variations in the information content of pixel neighborhoods not only in space but also in time. An appropriate distance metric between two collections of spatiotemporal salient points is introduced, which is based on the chamfer distance and an iterative linear time-warping technique that deals with time expansion or time-compression issues. A classification scheme that is based on relevance vector machines and on the proposed distance measure is proposed. Results on real image sequences from a small database depicting people performing 19 aerobic exercises are presented.
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Citations
Cites
Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472
Feature detection with automatic scale selection
Lindeberg, T. (1998) · International Journal of Computer Vision · doi:10.1023/a:1008045108935 · Cited by 2,541
Cited by
A survey on vision-based human action recognition
Poppe, R. (2010) · Image and Vision Computing · doi:10.1016/j.imavis.2009.11.014 · Cited by 2,242
Evaluation of local spatio-temporal features for action recognition
Wang, H., Ullah, M. M., Klaser, A., Laptev, I., & Schmid, C. (2009) · Procedings of the British Machine Vision Conference 2009 · doi:10.5244/c.23.124 · Cited by 1,296
Le, Q. V., Zou, W. Y., Yeung, S. Y., & Ng, A. Y. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995496 · Cited by 960
An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector
Willems, G., Tuytelaars, T., & Van Gool, L. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88688-4_48 · Cited by 869
Social signal processing: Survey of an emerging domain
Vinciarelli, A., Pantic, M., & Bourlard, H. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2008.11.007 · Cited by 842
Metadata
- Authors
- Oikonomopoulos, A., Patras, I., & Pantic, M.
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
- Published
- 2006-06
- Volume
- 36
- Issue
- 3
- Pages
- 710-719
- ISSN
- 1083-4419 (ISSN-L)
- 1941-0492
- OpenAlex
- W2140856208
- Cited by
- 240
- References
- 2
- FWCI
- 16.47
- Type
- journal article
- Language
- en
- Open access
- closed
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- Oikonomopoulos, A., Patras, I., & Pantic, M. (2006). Spatiotemporal salient points for visual recognition of human actions. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics). https://doi.org/10.1109/tsmcb.2005.861864
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