Virtual and Real World Adaptation for Pedestrian Detection

@article{Vzquez2013VirtualAR,
  title={Virtual and Real World Adaptation for Pedestrian Detection},
  author={David V{\'a}zquez and Antonio M. L{\'o}pez and Javier Mar{\'i}n and Daniel Ponsa and David Ger{\'o}nimo},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2013},
  volume={36},
  pages={797-809}
}
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… CONTINUE READING
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