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Cross-View Action Recognition via a Transferable Dictionary Pair

Published

Author(s)

P J. Phillips, Jingjing Zheng, Zhuolin Jiang, Rama Chellappa

Abstract

Discriminative appearance features are effective for recognizing actions in a fixed view, but generalize poorly to changes in viewpoint. We present a method for view- invariant action recognition based on sparse representations using a transferable dictio- nary pair. A transferable dictionary pair consists of two dictionaries that correspond to the source and target views respectively. The two dictionaries are learned simultaneously from pairs of videos taken at different views and aim to encourage each video in the pair to have the same sparse representation. Thus, the transferable dictionary pair links features between the two views that are useful for action recognition. Both unsupervised and supervised algorithms are presented for learning transferable dictionary pairs. Using the sparse representation as features, a classifier built in the source view can be directly transferred to the target view. We extend our approach to transferring an action model learned from multiple source views to one target view. We demonstrate the effectiveness of our approach on the multi-view IXMAS data set. Our results compare favorably to the the state of the art.
Conference Dates
September 10-12, 2012
Conference Location
Surrey
Conference Title
British Machine Vision Conference

Keywords

face recognition, dictionary learning

Citation

Phillips, P. , Zheng, J. , Jiang, Z. and Chellappa, R. (2012), Cross-View Action Recognition via a Transferable Dictionary Pair, British Machine Vision Conference, Surrey, -1, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=912109 (Accessed April 12, 2024)
Created September 12, 2012, Updated February 19, 2017