Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4969936 | Pattern Recognition | 2016 | 8 Pages |
Abstract
Action recognition in unconstrained videos is one of the most important challenges in computer vision. In this paper, we propose sparsity-inducing dictionaries as an effective representation for action classification in videos. We demonstrate that features obtained from sparsity based representation provide discriminative information useful for classification of action videos into various action classes. We show that the constructed dictionaries are distinct for a large number of action classes resulting in a significant improvement in classification accuracy on the HMDB51 dataset. We further demonstrate the efficacy of dictionaries and sparsity based classification on other large action video datasets like UCF50.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Debaditya Roy, Srinivas M., Krishna Mohan C.,