Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2017: administrator
Metadata Last Update2020: administrator
Citation KeyGattoJúniSant:2017:OrHaSu
TitleOrthogonal Hankel Subspaces for Applications in Gesture Recognition
DateOct. 17-20, 2017
Access Date2021, Jan. 21
Number of Files1
Size571 KiB
Context area
Author1 Gatto, Bernardo Bentes
2 Júnior, Waldir Sabino da Silva
3 Santos, Eulanda Miranda dos
Affiliation1 Federal University of Amazonas
2 Federal University of Amazonas
3 Federal University of Amazonas
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylčne
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, Joăo Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2017-08-22 11:51:47 :: -> administrator :: 2017
2020-02-19 02:01:42 :: administrator -> :: 2017
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
KeywordsHankel matrix, subspace method, gesture recognition.
AbstractGesture 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.
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Next Higher Units8JMKD3MGPAW/3PJT9LS
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