Detection of static moving objects using multiple nonparametric background models

Detection of moving objects remaining static is a fundamental step in many computer vision applications, since it allows to identify potentially dangerous situations (abandoned objects) and people temporally static. Here, we propose a strategy to efficiently detect such static moving objects, which is based on three nonparametric background models (long term, medium term and short term) to detect moving objects and a novel Finite State Machine to identify when a moving object becomes static.