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Exploiting human actions and object context for recognition tasks
Abstract
Our goal is to exploit human motion and object context to perform action recognition and object classification. Towards this end, we introduce a framework for recognizing actions and objects by measuring image-, object- and action-based information from video. Hidden Markov models are combined with object context to classify hand actions, which are aggregated by a Bayesian classifier to summarize activities. We also use Bayesian methods to differentiate the class of unknown objects by evaluating detected actions along with low-level, extracted object features. Our approach is appropriate for locating and classifying objects under a variety of conditions including full occlusion. We show experiments where both familiar and previously unseen objects are recognized using action and context information.
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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
HUMAN–ROBOT COLLABORATION: A SURVEY
BAUER, A., WOLLHERR, D., & BUSS, M. (2008) · International Journal of Humanoid Robotics · doi:10.1142/s0219843608001303 · Cited by 493
Understanding egocentric activities
Fathi, A., Farhadi, A., & Rehg, J. M. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126269 · Cited by 384
Mihailidis, A., Carmichael, B., & Boger, J. (2004) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2004.834386 · Cited by 208
Mining models of human activities from the web
Perkowitz, M., Philipose, M., Fishkin, K., & Patterson, D. J. (2004) · Proceedings of the 13th international conference on World Wide Web · doi:10.1145/988672.988750 · Cited by 183
Expectation grammars: leveraging high-level expectations for activity recognition
Minnen, D., Essa, I., & Starner, T. (2003) · 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. · doi:10.1109/cvpr.2003.1211525 · Cited by 110
Self-citationProgressively Parsing Interactional Objects for Fine Grained Action Detection
Ni, B., Yang, X., & Gao, S. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.116 · Cited by 69
Automatic video monitoring system for assessment of Alzheimer's disease symptoms
Romdhane, R., Mulin, E., Derreumeaux, A., Zouba, N., Piano, J., Lee, L., Leroi, I., Mallea, P., David, R., Thonnat, M., et al. (2012) · The Journal of nutrition, health and aging · doi:10.1007/s12603-012-0039-7 · Cited by 32
Recognizing object affordances in terms of spatio-temporal object-object relationships
Pieropan, A., Ek, C. H., & Kjellstrom, H. (2014) · 2014 IEEE-RAS International Conference on Humanoid Robots · doi:10.1109/humanoids.2014.7041337 · Cited by 16
Rogers, W. A., Essa, I. A., & Fisk, A. D. (2007) · Ergonomics in Design: The Quarterly of Human Factors Applications · doi:10.1177/106480460701500303 · Cited by 12
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Metadata
- Authors
- Moore, D. J., Essa, I. A., & Hayes, M. H.
- Venue
- Proceedings of the Seventh IEEE International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 1999
- Pages
- 80-86 vol.1
- OpenAlex
- W2098517267
- Cited by
- 281
- References
- 0
- FWCI
- 5.86
- Type
- conference paper
- Language
- en
- Open access
- closed
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