Learning Appearance in Virtual Scenarios for Pedestrian Detection

Detecting pedestrians in images is a key functionality to avoid vehicle-to-pedestrian collisions. The most promising detectors rely on appearance-based pedestrian classifiers trained with labelled samples. This paper addresses the following question: can a pedestrian appearance model learnt in virtual scenarios work successfully for pedestrian detection in real images? (Fig. 1). Our experiments suggest a positive answer, which is a new and relevant conclusion for research in pedestrian detection. More specifically, we record training sequences in virtual scenarios and then appearance-based pedestrian classifiers are learnt using HOG and linear SVM. We test such classifiers in a publicly available dataset provided by Daimler AG for pedestrian detection benchmarking. This dataset contains real world images acquired from a moving car. The obtained result is compared with the one given by a classifier learnt using samples coming from real images. The comparison reveals that, although...

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Francisco Marin Tur, David Vazquez, David Geronimo, Antonio M. Lopez
• Keywords: Appearance-based Pedestrian Classifiers, Computer Vision, CVPR 2010, Pedestrian, Pedestrian Detection