A Bayesian Approach on People Localization in Multicamera Systems

In this paper, we introduce a Bayesian approach on multiple people localization in multicamera systems. First, pixel-level features are extracted, which are based on physical properties of the 2-D image formation process, and provide information about the head and leg positions of the pedestrians, distinguishing standing and walking people, respectively. Then, features from the multiple camera views are fused to create evidence for the location and height of people in the ground plane. This evidence accurately estimates the leg position even if either the area of interest is only a part of the scene or the overlap ratio of the silhouettes from irrelevant outside motions with the monitored area is significant. Using this information, we create a 3-D object configuration model in the real world. We also utilize a prior geometrical constraint, which describes the possible interactions between two pedestrians. To approximate the position of the people, we use a population of 3-D cylinder objects, which is realized by a marked point process. The final configuration results are obtained by an iterative stochastic energy optimization algorithm. The proposed approach is evaluated on two publicly available datasets, and compared to a recent state-of-the-art technique. To obtain relevant quantitative test results, a 3-D ground truth annotation of the real pedestrian locations is prepared, while two different error metrics and various parameter settings are proposed and evaluated showing the advantages of our proposed model.