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Submitted on Nov 06, 2014

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Author : XRCE

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9 July 2014

Speaker: Antonio López, associate professor, Universitat Autònoma de BarcelonaBarcelona, Spain:


Virtual and Real World Adaptation for Pedestrian Detection

Pedestrian detection is of paramount interest for many applications.  Most promising detectors rely on classifiers 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 some real-world datasets, but it also appears the dataset shift problem in many others.