Detection of stationary foreground objects using multiple nonparametric background-foreground models on a finite state machine

Cuevas Rodríguez, Carlos and Martínez Sanz, Raquel and Berjón Díez, Daniel and García Santos, Narciso (2017). Detection of stationary foreground objects using multiple nonparametric background-foreground models on a finite state machine. "IEEE Transactions on image processing", v. 26 (n. 3); pp. 1127-1142. ISSN 1057-7149. https://doi.org/10.1109/TIP.2016.2642779.

Description

Title: Detection of stationary foreground objects using multiple nonparametric background-foreground models on a finite state machine
Author/s:
  • Cuevas Rodríguez, Carlos
  • Martínez Sanz, Raquel
  • Berjón Díez, Daniel
  • García Santos, Narciso
Item Type: Article
Título de Revista/Publicación: IEEE Transactions on image processing
Date: March 2017
Volume: 26
Subjects:
Freetext Keywords: Stationary foreground object, abandoned object, removed object, background subtraction, nonparametric modeling, background, foreground, finite state machine
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

There is a huge proliferation of surveillance systems that require strategies for detecting different kinds of stationary foreground objects (e.g., unattended packages or illegally parked vehicles). As these strategies must be able to detect foreground objects remaining static in crowd scenarios, regardless of how long they have not been moving, several algorithms for detecting different kinds of such foreground objects have been developed over the last decades. This paper presents an efficient and highquality strategy to detect stationary foreground objects, which is able to detect not only completely static objects but also partially static ones. Three parallel nonparametric detectors with different absorption rates are used to detect currently moving foreground objects, short-term stationary foreground objects, and long-term stationary foreground objects. The results of the detectors are fed into a novel finite state machine that classifies the pixels among background, moving foreground objects, stationary foreground objects, occluded stationary foreground objects, and uncovered background. Results show that the proposed detection strategy is not only able to achieve high quality in several challenging situations but it also improves upon previous strategies.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTEC2013-48453MR-UHDTVUnspecifiedUnspecified
Government of SpainTEC2016-75981IVMEUnspecifiedUnspecified

More information

Item ID: 50852
DC Identifier: http://oa.upm.es/50852/
OAI Identifier: oai:oa.upm.es:50852
DOI: 10.1109/TIP.2016.2642779
Official URL: https://ieeexplore.ieee.org/document/7792601/
Deposited by: Memoria Investigacion
Deposited on: 29 May 2018 15:45
Last Modified: 04 Apr 2019 11:46
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