Calibration and object correspondence in camera networks with widely separated overlapping views

This study contributes in two ways to the research of multi-camera object tracking in the context of visual surveillance. Firstly, a semi-automatic scene calibration method is proposed to deal with mapping a network of cameras with overlapped fields of view onto a single ground plane view, even when the overlap is not substantial. The proposed method uses a semi-supervised approach that combines tracked blobs with user-selected line scene features to recover the homographies between camera views that are both simple and accurate. Then, based on the scene calibration information, the intersection points of the projected vertical axis of single camera blobs are used to make object correspondences across multiple views. The method works in mixed environments of both pedestrians and vehicles, and is shown to be accurate and robust against segmentation noise and occlusions. Finally, the advantage of the proposed method is demonstrated by quantitative tracking performance evaluation and comparison against previous methods.