contributor author | Vazquez, David | |
contributor author | Lopez, Antonio M. | |
contributor author | Marin, J. | |
contributor author | Ponsa, Daniel | |
contributor author | Geronimo, David | |
date accessioned | 2020-03-12T18:27:00Z | |
date available | 2020-03-12T18:27:00Z | |
date issued | 2014 | |
identifier issn | 0162-8828 | |
identifier other | 6587038.pdf | |
identifier uri | http://libsearch.um.ac.ir:80/fum/handle/fum/958888?show=full | |
format | general | |
language | English | |
publisher | IEEE | |
title | Virtual and Real World Adaptation for Pedestrian Detection | |
type | Journal Paper | |
contenttype | Metadata Only | |
identifier padid | 7991386 | |
subject keywords | image classification | |
subject keywords | learning (artificial intelligence) | |
subject keywords | object detection | |
subject keywords | pedestrians | |
subject keywords | virtual reality | |
subject keywords | V-AYLA | |
subject keywords | adapted pedestrian classifier | |
subject keywords | domain adaptation framework | |
subject keywords | human-provided pedestrian annotations | |
subject keywords | object detector | |
subject keywords | pedestrian appearance model | |
subject keywords | pedestrian detection | |
subject keywords | pedestrian samples | |
subject keywords | real-world images | |
subject keywords | realistic virtual worlds | |
subject keywords | source domain | |
subject keywords | target domain | |
subject keywords | virtual-world based training | |
subject keywords | Accuracy | |
subject keywords | Cameras | |
subject keywords | Detectors | |
subject keywords | Image resolution | |
subject keywords | Interpolation | |
subject keywords | Testing | |
subject keywords | Training | |
subject keywords | Pedestrian d | |
identifier doi | 10.1109/TPAMI.2013.163 | |
journal title | Pattern Analysis and Machine Intelligence, IEEE Transactions on | |
journal volume | 36 | |
journal issue | 4 | |
filesize | 2253065 | |
citations | 0 | |