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Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
Abstract
Imagine a video taken on a sunny beach, can a computer automatically tell what is happening in the scene? Can it identify different human activities in the video, such as water surfing, people walking and lying on the beach? To automatically classify or localize different actions in video sequences is very useful for a variety of tasks, such as video surveillance, objectlevel video summarization, video indexing, digital library organization, etc. However, it remains a challenging task for computers to achieve robust action recognition due to cluttered background, camera motion, occlusion, and geometric and photometric variances of objects. For example, in a live video of a skating competition, the skater moves rapidly across the rink, and the camera also moves to follow the skater. With moving camera, non-stationary background, and moving target, few vision algorithms could identify, categorize and
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Citations
Cited by
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
Automatic annotation of human actions in video
Duchenne, O., Laptev, I., Sivic, J., Bach, F., & Ponce, J. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459279 · Cited by 275
Progressively 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
Metadata
- Authors
- Niebles, J. C., Wang, H., Wang, H., & Fei-Fei, L.
- Venue
- Procedings of the British Machine Vision Conference 2006
- Publisher
- British Machine Vision Association
- Published
- 2006
- Pages
- 127.1-127.10
- OpenAlex
- W2068576236
- Cited by
- 385
- References
- 0
- FWCI
- 40.15
- Type
- conference paper
- Language
- en
- Open access
- closed
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
