This paper presents novel approaches for background modeling, occlusion handling and event recognition by using multi-camera configurations that can be used to overcome the limitations of the single camera configurations. The main novelty in proposed background modeling approach is building multivariate Gaussians background model for each pixel of the reference camera by utilizing homography-related positions. Also, occlusion handling is achieved by generation of the top-view via trifocal tensors, as a result of matching over-segmented regions instead of pixels. The resulting graph is segmented into objects after determining the minimum spanning tree of this graph. Tracking of multi-view data is obtained by utilizing measurements across the views in case of occlusions. The last contribution is the classification of the resulting trajectories by GM-HMMs, yielding better results for using together all different view trajectories of the same object. Hence, multi-camera sensing is fully exploited from motion detection to event modeling.