Dissimilarity-based action recognition with the pair hidden Markov support vector machine

Wang, Z; Piccardi, M

Dissimilarity-based action recognition with the pair hidden Markov support vector machine

Wang, Z Piccardi, M

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2017 IEEE 19th International Workshop on Multimedia Signal Processing, 2017, pp. 1 - 6
Issue Date:: 2017-10-16

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Field	Value	Language
dc.contributor.author	Wang, Z	en_US
dc.contributor.author	Piccardi, M https://orcid.org/0000-0001-9250-6604	en_US
dc.date	2017-10-16	en_US
dc.date.issued	2017-10-16	en_US
dc.identifier.citation	2017 IEEE 19th International Workshop on Multimedia Signal Processing, 2017, pp. 1 - 6	en_US
dc.identifier.isbn	978-1-5090-3649-3	en_US
dc.identifier.issn	2473-3628	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121839
dc.description.abstract	Human action recognition in video is highly challenging due to the substantial variations in motion performance, recording settings and inter-personal differences. Most current research focuses on the extraction of effective features and the design of suitable classifiers. Conversely, in this paper we tackle this problem by a dissimilarity-based approach where classification is performed in terms of minimum distance from templates. To measure the dissimilarity between any two action instances, we propose leveraging the Pair Hidden Markov Support Vector Machine (PHMM-SSVM) that was recently proposed for tasks of video alignment. The main advantages of PHMM-SSVM are its ability to learn optimal alignment models from training sets of manually-aligned action pairs and provide alignment scores that can be used for action classification. The experimental results over two popular action datasets show that the proposed approach has been capable of achieving an accuracy higher than many existing methods and comparable to a state-of-the-art algorithm.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	2017 IEEE 19th International Workshop on Multimedia Signal Processing	en_US
dc.relation.ispartof	IEEE International Workshop on Multimedia Signal Processing	en_US
dc.relation.isbasedon	10.1109/MMSP.2017.8122257	en_US
dc.rights	© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Dissimilarity-based action recognition with the pair hidden Markov support vector machine	en_US
dc.type	Conference Proceeding
utslib.location	Piscataway, USA	en_US
utslib.location.activity	Luton, UK	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access	*
pubs.consider-herdc	true	en_US
pubs.finish-date	2017-10-18	en_US
pubs.place-of-publication	Piscataway, USA	en_US
pubs.publication-status	Published	en_US
pubs.start-date	2017-10-16	en_US

Abstract:

Human action recognition in video is highly challenging due to the substantial variations in motion performance, recording settings and inter-personal differences. Most current research focuses on the extraction of effective features and the design of suitable classifiers. Conversely, in this paper we tackle this problem by a dissimilarity-based approach where classification is performed in terms of minimum distance from templates. To measure the dissimilarity between any two action instances, we propose leveraging the Pair Hidden Markov Support Vector Machine (PHMM-SSVM) that was recently proposed for tasks of video alignment. The main advantages of PHMM-SSVM are its ability to learn optimal alignment models from training sets of manually-aligned action pairs and provide alignment scores that can be used for action classification. The experimental results over two popular action datasets show that the proposed approach has been capable of achieving an accuracy higher than many existing methods and comparable to a state-of-the-art algorithm.

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http://hdl.handle.net/10453/121839