Robust multiple cameras pedestrian detection with multi-view Bayesian network
A multi-view Bayesian network is proposed to model pedestrian candidates and their occlusion relationships in all views. A parameter learning algorithm is developed for MvBN by using a set of auxiliary, real-valued, and continuous variables. A height-adaptive projection is proposed to make the final detection robust to synthesis noises and calibration errors. Our approach is recognized as the best performer in five PETS evaluations from 2009 to 2013.
multiple cameras pedestrian detection multi-view model bayesian inference height adaptive projection