Attentive monitoring of multiple video streams driven by a Bayesian foraging strategy
This addresses the challenge of multi-stream video summarization and surveillance for applications like activity analysis, but it is incremental as it builds on existing foraging concepts.
The paper tackles the problem of efficiently monitoring multiple video streams by formalizing it as a foraging problem and proposing a Bayesian probabilistic framework to model attentive behavior, achieving experimental results on the UCR Videoweb Activities Dataset.
In this paper we shall consider the problem of deploying attention to 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 multi-stream 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 Dataset, a publicly available dataset, are presented to illustrate the utility of the proposed technique.