Pedestrian detection in low resolution night vision images

Abstract

This paper presents a test of pedestrian detection in low resolution night vision infrared images. An image feature extractor based on histograms of oriented gradients followed by a Support Vector Machine (SVM) classifier are evaluated, optimized and used. Tests performed on three different night vision infrared datasets show that the classification quality of the proposed method is very high even in very low resolutions of images. In practice, large frame size for analysis not always improves the classification effectiveness, but always requires more time for processing.