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Reference TypeConference Paper (Conference Proceedings)
Last Update2013:
Metadata Last Update2020: administrator
Citation KeySadMotMacVieAra:2013:TeMoDe
TitleA Tensor Motion Descriptor Based on Multiple Gradient Estimators
DateAug. 5-8, 2013
Access Date2021, Jan. 19
Number of Files1
Size241 KiB
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Author1 Sad, Dhiego
2 Mota, Virgínia Fernandes
3 Maciel, Luiz Maurílio
4 Vieira, Marcelo Bernardes
5 Araújo, Arnaldo de Albuquerque
Affiliation1 Universidade Federal de Juiz de Fora
2 Universidade Federal de Minas Gerais
3 Universidade Federal de Juiz de Fora
4 Universidade Federal de Juiz de Fora
5 Universidade Federal de Minas Gerais
EditorBoyer, Kim
Hirata, Nina
Nedel, Luciana
Silva, Claudio
Conference NameConference on Graphics, Patterns and Images, 26 (SIBGRAPI)
Conference LocationArequipa, Peru
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2013-07-11 04:39:29 :: -> administrator ::
2020-02-19 03:09:22 :: administrator -> :: 2013
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Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
KeywordsMultifilter analysis, Motion descriptor, Orientation tensor, Human action recognition.
AbstractThis work presents a novel approach for motion description in videos using multiple band-pass filters which act as first order derivative estimators. The filters response on each frame are coded into individual histograms of gradients to reduce their dimensionality. They are combined using orientation tensors. No local features are extracted and no learning is performed, i.e., the descriptor depends uniquely on the input video. Motion description can be enhanced even using multiple filters with similar or overlapping frequency response. For the problem of human action recognition using the KTH database, our descriptor achieved the recognition rate of 93.3% using three Daubechies filters, one extra filter designed to correlate them, two-fold protocol and a SVM classifier. It is superior to most global descriptor approaches and fairly comparable to the state- of-the-art methods.
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