A framework is proposed that answers the following question: if a moving object is observed by one camera in a pan-tilt-zoom (PTZ) camera network, what other camera(s) might be foveated on that object within a predefined time window, and what would be the corresponding PTZ parameter settings? No calibration is assumed, and there are no restrictions on camera placement or initial parameter settings. The framework accrues a predictive model over time. To start out, the cameras follow randomized "tours" in discretized PTZ space. If a moving object is detected in the field of view of more than one camera at a particular instant or within a predefined time window, then the model is updated to record the cameras' associations and the corresponding parameter settings. As more and more moving objects are observed, the model adapts and the most frequent associations are discovered. The formulation also allows for verification of its predictions, and reinforces its correct predictions. The system is demonstrated in observing people in an office environment with a three PTZ camera network.
['Uğur M. Erdem', 'Stan Sclaroff']
Look there! Predicting where to look for motion in an active camera network