People detection in complex scene using a cascade of boosted classifiers based on Haar-like-features
Muaad Hammuda Siala, Nawrès Khlifa, François Brémond, Kamel Hamrouni · 2009
Pedestrian detection in a real scene is an interesting application for video surveillance systems. This paper presents our contribution to improve the work of Viola and Jones, originally designed to detect faces. This work uses a cascade of classifiers based on Adaboost using Haar features. It improves the learning step by including a decision tree presenting the different poses and possible occlusions. The method has been tested on real and complex sequences and has given a good detection despite occlusions and poses variation.