Evaluation of background/foreground segmentation methods for multi-view occupancy maps

Abstract

An occupancy map provides a top view of a scene and can be used for monitoring the activity of people. We estimate occupancy maps using foreground silhouettes from multiple camera views. The ground occupancies computed from each view are fused in a Dempster-Shafer framework. However, it is not clear which background/foreground segmentation method for deriving the silhouettes is most suited for estimating our occupancy maps. We evaluated three segmentation methods from literature (ViBe, gaussian mixture model, method by Petrovic et al.), and one new segmentation method based on the analysis of edges. Occupancy maps were calculated for the APIDIS dataset, and the obtained maps were evaluated using the players' ground truth positions. We found that all methods perform similar in terms of the accuracy of the estimated maps, except the edges based segmentation method which outperforms all other methods. Future work will include texture based segmentation methods, and will focus on robustness with regard to lighting changes