Fast Object Detection with Occlusions

Abstract

We describe a new framework, based on boosting algorithms and cascade structures , to efficiently detect objects/faces with occlusions. While our approach is motivated by the work of Viola and Jones, several techniques have been developed for establishing a more general system, including (i) a robust boosting scheme , to select useful weak learners and to avoid overfitting; (ii) reinforcement training , to reduce false-positive rates via a more effective training procedure for boosted cascades; and (iii) cascading with evidence , to extend the system to handle occlusions, without compromising in detection speed. Experimental results on detecting faces under various situations are provided to demonstrate the performances of the proposed method.