Vehicle detection using bayesian enhanced CoBE classification

Abstract

This paper presents a noval computational framework, BN+CoBE, Bayesian enhaced Cascades of Boosted Ensemble, for on-road vehicle detection. The objective of this research is to reduce false alarms while keeping the detection rate high. In the proposed system, BN+CoBE, the CoBE is trained on image texture features and Bayesian conditional probability function is trained on vehicle features of location, size and confidence values generated by all the stages in CoBE. Experiment results on real world data show that the proposed BN+CoBE system is effective in reducing false alarms significantly while keeping the detection rate high. © 2010 Springer-Verlag Berlin Heidelberg.