@InProceedings{GattoJśniSant:2017:OrHaSu,
author = "Gatto, Bernardo Bentes and J{\'u}nior, Waldir Sabino da Silva and
Santos, Eulanda Miranda dos",
affiliation = "{Federal University of Amazonas} and {Federal University of
Amazonas} and {Federal University of Amazonas}",
title = "Orthogonal Hankel Subspaces for Applications in Gesture
Recognition",
booktitle = "Proceedings...",
year = "2017",
editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and
Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and
Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba,
Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo
and Vital, Creto and Pagot, Christian Azambuja and Petronetto,
Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "Hankel matrix, subspace method, gesture recognition.",
abstract = "Gesture recognition is an important research area in video
analysis and computer vision. Gesture recognition systems include
several advantages, such as the interaction with machines without
needing additional external devices. Moreover, gesture recognition
involves many challenges, as the distribution of a specific
gesture largely varies depending on viewpoints due to its multiple
joint structures. In this paper, We present a novel framework for
gesture recognition. The novelty of the proposed framework lies in
three aspects: first, we propose a new gesture representation
based on a compact trajectory matrix, which preserves spatial and
temporal information. We understand that not all images of a
gesture video are useful for the recognition task, therefore it is
necessary to create a method where it is possible to detect the
images that do not contribute to the recognition task, decreasing
the computational cost of the overall framework. Second, we
represent this compact trajectory matrix as a subspace, achieving
discriminative information, as the trajectory matrices obtained
from different gestures generate dissimilar clusters in a low
dimension space. Finally, we introduce an automatic procedure to
infer the optimal dimension of each gesture subspace. We show that
our compact representation presents practical and theoretical
advantages, such as compact representation and low computational
requirements. We demonstrate the advantages of the proposed method
by experimentation employing Cambridge gesture and Human-Computer
Interaction datasets.",
conference-location = "Niter{\'o}i, RJ, Brazil",
conference-year = "17-20 Oct. 2017",
doi = "10.1109/SIBGRAPI.2017.63",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.63",
language = "en",
ibi = "8JMKD3MGPAW/3PFTPP2",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3PFTPP2",
targetfile = "Hankel_Subspace.pdf",
urlaccessdate = "2024, Apr. 29"
}