@inproceedings{7f6498fdd95f42799995b61e4228d31a,
title = "Human action recognition in video by fusion of structural and spatio-temporal features",
abstract = "The problem of human action recognition has received increasing attention in recent years for its importance in many applications. Local representations and in particular STIP descriptors have gained increasing popularity for action recognition. Yet, the main limitation of those approaches is that they do not capture the spatial relationships in the subject performing the action. This paper proposes a novel method based on the fusion of global spatial relationships provided by graph embedding and the local spatio-temporal information of STIP descriptors. Experiments on an action recognition dataset reported in the paper show that recognition accuracy can be significantly improved by combining the structural information with the spatio-temporal features.",
author = "{Zare Borzeshi}, Ehsan and {Perez Concha}, Oscar and Massimo Piccardi",
year = "2012",
doi = "10.1007/978-3-642-34166-3_52",
language = "English",
isbn = "9783642341656",
volume = "7626 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer, Springer Nature",
pages = "474--482",
editor = "Georgy Gimel'farb and Edwin Hancock and Atsushi Imiya and Arjan Kuijper and Mineichi Kudo and Shinichiro Omachi and Terry Windeatt and Keiji Yamada",
booktitle = "Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, SSPR and SPR 2012, Proceedings",
address = "United States",
note = "Joint IAPR International Workshops on Structural and Syntactic PatternRecognition, SSPR 2012 and Statistical Techniques in Pattern Recognition,SPR 2012 ; Conference date: 07-11-2012 Through 09-11-2012",
}