Information-driven Surveillance (Talk)

The supervision of public spaces aims at multiple objectives, such as early acquisition of targets, their identification and pursuit throughout the supervised area. To achieve these, typical sensors such as pan-tilt-zoom cameras need to either focus on individuals, or provide a broad field of view, which are conflicting control settings. We address this problem in an information-theoretic manner: by phrasing each of the objectives in terms of mutual information, they become comparable. The problem turns into maximisation of information, which is predicted for the next time step and phrased as a decision process.

Our approach results in decisions that on average satisfy objectives in desired proportions. At the end of the talk I will address an application of information maximisation to aid in the interactive calibration of cameras.