Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/77411
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Type: Conference paper
Title: Bilinear programming for human activity recognition with unknown MRF graphs
Author: Wang, Z.
Shi, Q.
Shen, C.
Van Den Hengel, A.
Citation: Proceedings, 2013 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013, 23-28 June 2013, Portland, Oregon, USA: pp. 1690-1697
Publisher: IEEE
Publisher Place: United States of America
Issue Date: 2013
Series/Report no.: IEEE Conference on Computer Vision and Pattern Recognition
ISBN: 9780769549897
ISSN: 1063-6919
Conference Name: IEEE Conference on Computer Vision and Pattern Recognition (26th : 2013 : Portland, Oregon)
Statement of
Responsibility: 
Zhenhua Wang, Qinfeng Shi, Chunhua Shen and Anton van den Hengel
Abstract: Markov Random Fields (MRFs) have been successfully applied to human activity modelling, largely due to their ability to model complex dependencies and deal with local uncertainty. However, the underlying graph structure is often manually specified, or automatically constructed by heuristics. We show, instead, that learning an MRF graph and performing MAP inference can be achieved simultaneously by solving a bilinear program. Equipped with the bilinear program based MAP inference for an unknown graph, we show how to estimate parameters efficiently and effectively with a latent structural SVM. We apply our techniques to predict sport moves (such as serve, volley in tennis) and human activity in TV episodes (such as kiss, hug and Hi-Five). Experimental results show the proposed method outperforms the state-of-the-art.
Keywords: Human activity recognition; MRF; bilinear programming, linear programming
Rights: ©IEEE
RMID: 0020133292
DOI: 10.1109/CVPR.2013.221
Description (link): http://www.pamitc.org/cvpr13/
Appears in Collections:Computer Science publications

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