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Virtual and Real World Adaptation for Pedestrian Detection

contributor authorVazquez, David
contributor authorLopez, Antonio M.
contributor authorMarin, J.
contributor authorPonsa, Daniel
contributor authorGeronimo, David
date accessioned2020-03-12T18:27:00Z
date available2020-03-12T18:27:00Z
date issued2014
identifier issn0162-8828
identifier other6587038.pdf
identifier urihttps://libsearch.um.ac.ir:443/fum/handle/fum/958888?locale-attribute=fa&show=full
formatgeneral
languageEnglish
publisherIEEE
titleVirtual and Real World Adaptation for Pedestrian Detection
typeJournal Paper
contenttypeMetadata Only
identifier padid7991386
subject keywordsimage classification
subject keywordslearning (artificial intelligence)
subject keywordsobject detection
subject keywordspedestrians
subject keywordsvirtual reality
subject keywordsV-AYLA
subject keywordsadapted pedestrian classifier
subject keywordsdomain adaptation framework
subject keywordshuman-provided pedestrian annotations
subject keywordsobject detector
subject keywordspedestrian appearance model
subject keywordspedestrian detection
subject keywordspedestrian samples
subject keywordsreal-world images
subject keywordsrealistic virtual worlds
subject keywordssource domain
subject keywordstarget domain
subject keywordsvirtual-world based training
subject keywordsAccuracy
subject keywordsCameras
subject keywordsDetectors
subject keywordsImage resolution
subject keywordsInterpolation
subject keywordsTesting
subject keywordsTraining
subject keywordsPedestrian d
identifier doi10.1109/TPAMI.2013.163
journal titlePattern Analysis and Machine Intelligence, IEEE Transactions on
journal volume36
journal issue4
filesize2253065
citations0


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