Attentive monitoring of multiple video streams driven by a Bayesian foraging strategy

TL;DR

This paper introduces a probabilistic foraging-based framework for attentively monitoring multiple video streams, enabling efficient data collection and potential applications in video summarization and surveillance.

Contribution

It formalizes attentive video stream monitoring as a Bayesian foraging problem, providing a novel probabilistic approach to focus on the most relevant streams and activities.

Findings

Effective stream selection demonstrated on UCR Videoweb Activities Dataset

Framework improves relevance of monitored data

Potential for enhanced video summarization and surveillance

Abstract

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.