Active inference for retrieval in camera networks

We address the problem of searching camera network videos to retrieve frames containing specified individuals. We show the benefit of utilizing a learned probabilistic model that captures dependencies among the cameras. In addition, we develop an active inference framework that can request human input at inference time, directing human attention to the portions of the videos whose correct annotation would provide the biggest performance improvements. Our primary contribution is to show that by mapping video frames in a camera network onto a graphical model, we can apply collective classification and active inference algorithms to significantly increase the performance of the retrieval system, while minimizing the number of human annotations required.