Vehicle detection using Bayesian Network Enhanced Cascades Classification (BNECC)

This paper presents a novel computational framework, BNECC, Bayesian Network Enhanced Cascades Classification, for on-road vehicle detection. The objective of this research is to combine the texture features with geometric features of objects in an object recognition system. BNECC consists of two tiers of classifiers. The first tier is a Cascade of Boosted Ensembles (CoBE) classifiers trained on object texture features. The second tier is a Bayesian network trained using features of vehicle location, size and the confidence values generated by all the stage classifiers in CoBE. Experiment results on real world data show that proposed BNECC framework is effective in reducing false alarms significantly while keeping the detection rate high.