Event prediction in a hybrid camera network

Given a hybrid camera layout—one containing, for example, static and active cameras—and people moving around following established traffic patterns, our goal is to predict a subset of cameras, respective camera parameter settings, and future time windows that will most likely lead to success the vision tasks, such as, face recognition when a camera observes an event of interest. We propose an adaptive probabilistic model that accrues temporal camera correlations over time as the cameras report observed events. No extrinsic, intrinsic, or color calibration of cameras is required. We efficiently obtain the camera parameter predictions using a modified Sequential Monte Carlo method. We demonstrate the performance of the model in an example face detection scenario in both simulated and real environment experiments, using several active cameras.

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