Tracking in uncalibrated cameras with overlapping field of view

To track people successfully in multiple cameras, one needs to establish correspondence between objects captured in each camera. We present a system for tracking people in multiple uncalibrated cameras. The system is able to discover spatial relationships between the camera fields of view and use this information to correspond between different perspective views of the same person. We employ the novel approach of finding the limits of field of view (FOV) of a camera as visible in the other cameras. Using this information, when a person is seen in one camera, we are able to predict all the other cameras in which this person will be visible. Moreover, we apply the FOV constraint to disambiguate between possible candidates for correspondence. Tracking in each individual camera needs to be resolved before such an analysis can be applied. We perform tracking in a single camera using background subtraction, followed by region correspondence. This takes into account the velocities, sizes and distance of bounding boxes obtained through connected component labeling. We present results on sequences taken from the PETS 2001 dataset, which contain several persons and vehicles simultaneously. The proposed approach is very fast compared to camera calibration based approaches.