Recognizing realistic actions from videos

11 years 1 months ago
Recognizing realistic actions from videos
In this paper, we present a systematic framework for recognizing realistic actions from videos “in the wild.” Such unconstrained videos are abundant in personal collections as well as on the web. Recognizing action from such videos has not been addressed extensively, primarily due to the tremendous variations that result from camera motion, background clutter, changes in object appearance, and scale, etc. The main challenge is how to extract reliable and informative features from the unconstrained videos. We extract both motion and static features from the videos. Since the raw features of both types are dense yet noisy, we propose strategies to prune these features. We use motion statistics to acquire stable motion features and clean static features. Furthermore, PageRank is used to mine the most informative static features. In order to further construct compact yet discriminative visual vocabularies, a divisive information-theoretic algorithm is employed to group semantically re...
Jingen Liu, Jiebo Luo, Mubarak Shah
Added 18 May 2010
Updated 13 Jul 2011
Type Conference
Year 2009
Where CVPR
Authors Jingen Liu, Jiebo Luo, Mubarak Shah
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