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Reference TypeConference Proceedings
Last Update2018: administrator
Metadata Last Update2020: administrator
Citation KeyMeloSaCaSoPeSc:2018:ObTeSe
TitleObject-based Temporal Segment Relational Network for Activity Recognition
DateOct. 29 - Nov. 1, 2018
Access Date2020, Dec. 04
Number of Files1
Size832 KiB
Context area
Author1 Melo, Victor Hugo Cunha de
2 Santos, Jesimon Barreto
3 Caetano Júnior, Carlos Antônio
4 Souza, Jéssica Sena de
5 Penatti, Otávio Augusto Bizetto
6 Schwartz, William Robson
Affiliation1 Smart Sense Laboratory, Universidade Federal de Minas Gerais
2 Smart Sense Laboratory, Universidade Federal de Minas Gerais
3 Smart Sense Laboratory, Universidade Federal de Minas Gerais
4 Smart Sense Laboratory, Universidade Federal de Minas Gerais
5 Advanced Technologies, Samsung Research Institute
6 Smart Sense Laboratory, Universidade Federal de Minas Gerais
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
History2018-09-04 04:48:50 :: -> administrator ::
2020-02-19 03:10:45 :: administrator -> :: 2018
Content and structure area
Is the master or a copy?is the master
Document Stagecompleted
Document Stagenot transferred
Content TypeExternal Contribution
Tertiary TypeFull Paper
KeywordsAction recognition, contextual cues, relational reasoning.
AbstractVideo understanding is the next frontier of computer vision, in which activity recognition plays a major role. Despite the recent improvements in holistic activity recognition, further researching part-based models such as context may allow us to better understand what is important for activities and thus improve our current activity recognition models. This work tackles contextual cues obtained from object detections, in which we posit that objects relevant to an action are related to its spatial arrangement regarding an agent. Based on that, we propose Egocentric Pyramid to encode such spatial relationships. We further extend it by proposing a data-centric approach named Temporal Segment Relational Network (TSRN). Our experiments give support to the hypothesis that object spatiality provides an important clue to activity recognition. In addition, our data-centric approach shows that besides such spatial features, there may be other important information that further enhances the object-based activity recognition, such as co-occurrence, relative size, and temporal information.
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Next Higher Units8JMKD3MGPAW/3RPADUS
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