Multi-camera Pedestrian Detection with Multi-view Bayesian Network Model

In this paper, we propose a novel method with the multi-view Bayesian network (MBN) model to detect pedestrians from multi-camera surveillance videos. In our method, the ground plane is discretized in a predefined set of locations and our aim is to estimate the occupancy probability of each location that can be then used to predict the occurrence of pedestrians. To reduce the possible phantoms, we use MBN to model the potential occlusion relationship of all locations in all views, and the “subjective supposing” node states (SSNS) as a set of Boolean parameters of MBN to denote whether a pedestrian occurs at the corresponding location. Thus a learning algorithm is proposed to estimate the SSNS parameters, by finding such a configuration that the final occupancy possibility can best explain the image observations (i.e., foreground masks) from different views. The experimental results on the APIDIS and PETS09 S2L1 benchmark datasets show that our method can obtain at least 10% performance gain compared with several state-of-the-art algorithms.