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|a 10.1109/TPAMI.2013.163
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|a eng
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| 100 |
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|a Vázquez, David
|e verfasserin
|4 aut
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| 245 |
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|a Virtual and Real World Adaptation for Pedestrian Detection
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|c 2014
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|a Text
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|a Date Completed 07.03.2016
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|a Date Revised 10.09.2015
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|a published: Print
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|a Citation Status MEDLINE
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|a Pedestrian detection is of paramount interest for many applications. Most promising detectors rely on discriminatively learnt classifiers, i.e., trained with annotated samples. However, the annotation step is a human intensive and subjective task worth to be minimized. By using virtual worlds we can automatically obtain precise and rich annotations. Thus, we face the question: can a pedestrian appearance model learnt in realistic virtual worlds work successfully for pedestrian detection in real-world images? Conducted experiments show that virtual-world based training can provide excellent testing accuracy in real world, but it can also suffer the data set shift problem as real-world based training does. Accordingly, we have designed a domain adaptation framework, V-AYLA, in which we have tested different techniques to collect a few pedestrian samples from the target domain (real world) and combine them with the many examples of the source domain (virtual world) in order to train a domain adapted pedestrian classifier that will operate in the target domain. V-AYLA reports the same detection accuracy than when training with many human-provided pedestrian annotations and testing with real-world images of the same domain. To the best of our knowledge, this is the first work demonstrating adaptation of virtual and real worlds for developing an object detector
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a López, Antonio M
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Marín, Javier
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ponsa, Daniel
|e verfasserin
|4 aut
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| 700 |
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|a Gerónimo, David
|e verfasserin
|4 aut
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 36(2014), 4 vom: 01. Apr., Seite 797-809
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|g volume:36
|g year:2014
|g number:4
|g day:01
|g month:04
|g pages:797-809
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|u http://dx.doi.org/10.1109/TPAMI.2013.163
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