Seminar: "Histograms of pattern sets for image classification, object recognition and image re-ranking"; July 9, 2014 at 11:00 in Mont Blanc

Speaker: Antonio Lopez, associate professor, Universitat Autònoma de Barcelona, Spain.

Abstract: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.

Accordingly, during the last years we have explored different domain adaptation ideas for several state-of-the-art pedestrian detection approaches. In this talk, we review this work. Therefore, we will report adaptation results for different features as HOG, LBP, Haar and EOH, different pedestrian models as the Holistic-SVM/AdaBoost and Deformable Part-Based Models (DPM), as well as different strategies as supervised/unsupervised and batch/incremental. Overall, we will see that we have been able to actually adapt virtual and real worlds.