Detection of static objects for the task of video surveillance
Detecting static objects in video sequences has a high relevance in many surveillance scenarios like airports and railwaystations. In this paper we propose a system for the detection of static objects in crowded scenes that, based on the detection of two background models learning at different rates, classifies pixels with the help of a finite-state machine. The background is modelled by two mixtures of Gaussians with identical parameters except for the learning rate. The state machine provides the meaning for the interpretation of the results obtained from background subtraction and can be used to incorporate additional information cues, obtaining thus a flexible system specially suitable for real-life applications. The system was built in our surveillance application and successfully validated with several public datasets.