Day and night pedestrian detection using cascade AdaBoost system

This paper presents the results of an all-day-long pedestrian classification system based on an AdaBoost cascade meta-algorithm. The underlying idea is to use a Haar-features-based AdaBoost together with an ad-hoc-features-based AdaBoost system in order to reach a better pedestrian classification. A specific night-time pedestrian classification is developed in order to obtain a system that can be used also in poorly illuminated environments. These classifiers are joined together using a cascade AdaBoost system that uses the output of the previous classifiers to obtain a final classification for the area. In the paper the night time and the ad-hoc features systems are presented together with the cascade classification and quantitative results.