A prediction-based distributed tracking protocol for video surveillance

Video surveillance is an important security enforcement operation in many contexts, from large public areas to private smart homes and smart buildings. Today's video surveillance systems are much more than mere recording storages, as the advancement in classification and recognition allow for an immediate target recognition without the intervention of human operators. These smart video surveillance systems usually rely on a central server as the main coordination of recognition and tracking, which can represent a performance or economical bottleneck. In this paper, our contribution focuses on a decentralized protocol with the aim of eliminating such bottleneck. Our protocol organizes the distribution of a classification library among the cameras involved, which also participate actively to the target recognition phase. The protocol minimizes the network overhead towards the centralized server while keeping high the speed of recognition making use of a system to predict the movements of the targets. We tested the protocol by means of simulations, exploiting a realistic indoor human mobility model.