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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPBW34M/3K2N5CH
Repositorysid.inpe.br/sibgrapi/2015/08.07.21.34
Last Update2015:08.07.21.34.53 (UTC) victorhcmelo@dcc.ufmg.br
Metadatasid.inpe.br/sibgrapi/2015/08.07.21.34.53
Metadata Last Update2016:06.03.21.18.38 (UTC) administrator
Citation KeyMeloMenoSchw:2015:FaRoOp
TitleFast and robust optimization approaches for pedestrian detection
FormatOn-line
Year2015
Access Date2021, Dec. 03
Secondary TypeMaster's Work
Number of Files1
Size2696 KiB
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Author1 Melo, Victor Hugo Cunha de
2 Menotti, David
3 Schwartz, William Robson
Affiliation1 Universidade Federal de Minas Gerais
2 Universidade Federal de Ouro Preto
3 Universidade Federal de Minas Gerais
EditorSegundo, Maurício Pamplona
Faria, Fabio Augusto
e-Mail Addressvictorhcmelo@dcc.ufmg.br
Conference NameConference on Graphics, Patterns and Images, 28 (SIBGRAPI)
Conference LocationSalvador
DateAug. 26-29, 2015
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeMaster's or Doctoral Work
History (UTC)2015-08-07 21:34:53 :: victorhcmelo@dcc.ufmg.br -> administrator ::
2016-06-03 21:18:38 :: administrator -> :: 2015
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Is the master or a copy?is the master
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Transferable1
KeywordsPedestrian detection
random filtering
location regression
cascade of rejection
partial least squares
AbstractThe large number of surveillance cameras available nowadays in strategic points of large cities aims to provide a safe environment. However, the huge amount of visual data provided by the cameras prevents its manual processing, requiring the application of automated methods. Among such methods, pedestrian detection plays an important role in reducing the amount of data. However, the currently available methods are unable to process such large amount of data in real time. Therefore, there is a need for the development of optimization techniques. Towards accomplishing the goal of reducing costs for pedestrian detection, this Masters thesis proposed two optimization approaches. Our first approach proposes a novel optimization that performs a random filtering in the image to select a small number of detection windows, allowing a reduction in the computational cost. Our results show that accurate results can be achieved even when a large number of detection windows are discarded. The second approach consists of a cascade of rejection based on Partial Least Squares (PLS) combined with the propagation of latent variables through the stages. Our results show that the method reduces the computational cost by increasing the number of rejected background samples in earlier stages of the cascade.
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Languageen
Target File2015-WTD-VictorMelo.submitted.pdf
User Groupvictorhcmelo@dcc.ufmg.br
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Next Higher Units8JMKD3MGPBW34M/3K24PF8
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