An Experimental Evaluation of Foreground Detection Algorithms in Real Scenes

Abstract

Foreground detection is an important preliminary step of many video analysis systems. Many algorithms have been proposed in the last years, but there is not yet a consensus on which approach is the most effective, not even limiting the problem to a single category of videos. This paper aims at constituting a first step towards a reliable assessment of the most commonly used approaches. In particular, four notable algorithms that perform foreground detection have been evaluated using quantitative measures to assess their relative merits and demerits. The evaluation has been carried out using a large, publicly available dataset composed by videos representing different realistic applicative scenarios. The obtained performance is presented and discussed, highlighting the conditions under which algorithm can represent the most effective solution.