Cross-View Action Recognition via Transferable Dictionary Learning

By Jingjing Zheng, Zhuolin Jiang, Ramalingam Chellappa. 2016

82 citations recorded by the source corpus.

Discriminative appearance features are effective for recognizing actions in a fixed view, but may not generalize well to a new view. In this paper, we present two effective approaches to learn dictionaries for robust action recognition across views. In the first approach, we learn a set of view-specific dictionaries where each dictionary corresponds to one camera view. These dictionaries are learned simultaneously from the sets of correspondence videos taken at different views with the aim of encouraging each video in the set to have the same sparse representation. In the second approach, we additionally learn a common dictionary shared by different views to model view-shared features. This approach represents the videos in each view using a view-specific dictionary and the common dictionary. More importantly, it encourages the set of videos taken from the different views of the same action to have the similar sparse representations. The learned common dictionary not only has the capability to represent actions from unseen views, but also makes our approach effective in a semi-supervised setting where no correspondence videos exist and only a few labeled videos exist in the target view. The extensive experiments using three public datasets demonstrate that the proposed approach outperforms recently developed approaches for cross-view action recognition.

Subfields: Artificial Intelligence, Biomedical Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

R-C3D: Region Convolutional 3D Network for Temporal Activity Detection (2017, background): “Activity Detection There is a long history of activity recognition, or classifying trimmed video clips into ﬁxed set of categories [11, 15, 19, 27, 33, 42].”