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

Cuevas Rodríguez, Carlos; Martínez Sanz, Raquel; Berjón Díez, Daniel y 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.

Descripción

Título: Detection of stationary foreground objects using multiple nonparametric background-foreground models on a finite state machine
Autor/es:
  • Cuevas Rodríguez, Carlos
  • Martínez Sanz, Raquel
  • Berjón Díez, Daniel
  • García Santos, Narciso
Tipo de Documento: Artículo
Título de Revista/Publicación: IEEE Transactions on image processing
Fecha: Marzo 2017
Volumen: 26
Materias:
Palabras Clave Informales: Stationary foreground object, abandoned object, removed object, background subtraction, nonparametric modeling, background, foreground, finite state machine
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Señales, Sistemas y Radiocomunicaciones
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Más información

ID de Registro: 50852
Identificador DC: http://oa.upm.es/50852/
Identificador OAI: oai:oa.upm.es:50852
Identificador DOI: 10.1109/TIP.2016.2642779
URL Oficial: https://ieeexplore.ieee.org/document/7792601/
Depositado por: Memoria Investigacion
Depositado el: 29 May 2018 15:45
Ultima Modificación: 29 May 2018 15:45
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