Pedestrian Detection Using Augmented Training Data

Abstract

Detecting pedestrians is a challenging and widely explored problem in computer vision. Many approaches rely on large quantities of manually labelled training data to learn a pedestrian classifier. To reduce the need for collecting and manually labelling real image training data, this paper investigates the possibility to use augmented images to train a pedestrian classifier. Augmented images are generated by rendering virtual pedestrians onto real image backgrounds. Classifiers learned from real or augmented training data are evaluated on real image test data from the widely used Daimler Mono Pedestrian benchmark data set. Results show that augmented training data generated from a single 200 frame image sequence reach 70 % average detection rate at one False Positives Per Image (FPPI), compared to 81 % for a classifier trained by a large-scale real data set. Results also show that complementing real training data with augmented data improves detection performance, compared to using real training data only.