title: Attentive Monitoring of Multiple Video Streams Driven by a Bayesian Foraging Strategy.
abstract: In this paper, we shall consider the problem of deploying attention to the subsets of the video streams for collating the most relevant data and information of interest related to a given task. We formalize this monitoring problem as a foraging problem. We propose a probabilistic framework to model observer's attentive behavior as the behavior of a forager. The forager, moment to moment, focuses its attention on the most informative stream/camera, detects interesting objects or activities, or switches to a more profitable stream. The approach proposed here is suitable to be exploited for multistream video summarization. Meanwhile, it can serve as a preliminary step for more sophisticated video surveillance, e.g., activity and behavior analysis. Experimental results achieved on the UCR Videoweb Activities Data Set, a publicly available data set, are presented to illustrate the utility of the proposed technique.
authors.forename: Paolo
authors.initials: P
authors.lastname: Napoletano
authors.forename: Giuseppe
authors.initials: G
authors.lastname: Boccignone
authors.forename: Francesco
authors.initials: F
authors.lastname: Tisato