|Journal||EURASIP Journal on Image and Video Processing|
|Publisher||Hindawi Publishing Corporation|
|Author(s)||Rubén Heras Evangelio, Thomas Sikora|
|Title||Static Object Detection Based on a Dual Background Model and a Finite-State Machine|
|Abstract||Detecting static objects in video sequences has a high relevance in many surveillance applications, such as the detection of abandoned objects in public areas. In this paper, we present a system for the detection of static objects in crowded scenes. Based on the detection of two background models learning at different rates, pixels are classified 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; it can be implemented as a lookup table with negligible computational cost and it can be easily extended. Due to the definition of the states in the state machine, the system can be used either full automatically or inter-actively, making it extremely suitable for real-life surveillance applications.
The system was successfully validated with several public datasets.
|Key words||static object detection, finite-state machine, crowded scenes, background subtraction, background modelling|
|Note||Article ID 858502, doi:10.1155/2011/858502|