Risk entropy modeling of surveillance camera for public security application

Abstract

Surveillance cameras are widely installed at public places around the world, and the video surveillance system plays an un-substitutable role in police work, especially in case investigation. The problem regarding the effectiveness and rationality of the video surveillance system comes into being in terms of its high demand for investment and rising public concern of over-construction potentially. To answer the question, it ought to establish mode and metrics for measuring effectiveness in theory. This article argued that the police video surveillance system is preferably a sensor network than a Physical Protect System (PPS) because its main feature is to provide the police officers with the visual information they need. Once the police cannot receive sufficient information from the system, decisions of public security are given based on limited or misleading information, and there may be some potential risks remained. Such risks of public security are not directly relevant to the integrity and value of the assets but the uncertainty of decision-making, which is different from the one of traditional PPS. In this paper, we proposed an entropy model for measuring the uncertainty based on attributions of video surveillance for law enforcement. Public security risk was divided into three types within the model according to the source of the risk, such as fixed targets (or restricted areas), moving objects, and video information quality. We verified the validity of the model by the simulation experiment of camera field optimization and discussed further work.