Actions in context
Top Cited Papers
- 1 June 2009
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 10636919,p. 2929-2936
- https://doi.org/10.1109/cvpr.2009.5206557
Abstract
This paper exploits the context of natural dynamic scenes for human action recognition in video. Human actions are frequently constrained by the purpose and the physical properties of scenes and demonstrate high correlation with particular scene classes. For example, eating often happens in a kitchen while running is more common outdoors. The contribution of this paper is three-fold: (a) we automatically discover relevant scene classes and their correlation with human actions, (b) we show how to learn selected scene classes from video without manual supervision and (c) we develop a joint framework for action and scene recognition and demonstrate improved recognition of both in natural video. We use movie scripts as a means of automatic supervision for training. For selected action classes we identify correlated scene classes in text and then retrieve video samples of actions and scenes for training using script-to-video alignment. Our visual models for scenes and actions are formulated within the bag-of-features framework and are combined in a joint scene-action SVM-based classifier. We report experimental results and validate the method on a new large dataset with twelve action classes and ten scene classes acquired from 69 movies.Keywords
This publication has 19 references indexed in Scilit:
- Learning realistic human actions from moviesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2008
- Hierarchical Recognition of Human Activities Interacting with ObjectsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2007
- What, where and who? Classifying events by scene and object recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2007
- Objects in ContextPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2007
- Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene CategoriesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- Unsupervised Learning of Human Action Categories Using Spatial-Temporal WordsPublished by British Machine Vision Association and Society for Pattern Recognition ,2006
- On Space-Time Interest PointsInternational Journal of Computer Vision, 2005
- Distinctive Image Features from Scale-Invariant KeypointsInternational Journal of Computer Vision, 2004
- Scale & Affine Invariant Interest Point DetectorsInternational Journal of Computer Vision, 2004
- Exploiting human actions and object context for recognition tasksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1999