Background-Subtraction Algorithm Optimization for Home Camera-Based Night-Vision Fall Detectors

Alonso, Mercedes and Brunete González, Alberto and Hernando Gutiérrez, Miguel and Gambao Galán, Ernesto (2019). Background-Subtraction Algorithm Optimization for Home Camera-Based Night-Vision Fall Detectors. "Ieee Access", v. 7 (n. 1); pp. 152399-152411. ISSN 2169-3536. https://doi.org/10.1109/ACCESS.2019.2948321.

Description

Title: Background-Subtraction Algorithm Optimization for Home Camera-Based Night-Vision Fall Detectors
Author/s:
  • Alonso, Mercedes
  • Brunete González, Alberto
  • Hernando Gutiérrez, Miguel
  • Gambao Galán, Ernesto
Item Type: Article
Título de Revista/Publicación: Ieee Access
Date: December 2019
ISSN: 2169-3536
Volume: 7
Subjects:
Freetext Keywords: Fall detection; camera-based; background-subtraction
Faculty: E.T.S.I. Industriales (UPM)
Department: Automática, Ingeniería Eléctrica y Electrónica e Informática Industrial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Background subtraction is one of the key pre-processing steps necessary for obtaining relevant information from a video sequence. The selection of a background subtraction algorithm and its parameters is also important for achieving optimal detection performance, especially in night environments. The research contribution presented in this paper is the identification of the optimal background subtractor algorithm in indoor night-time environments, with a focus on the detection of human falls. 30 background subtraction algorithms are analyzed to determine which has the best performance in indoor night-time environments. Genetic algorithms have been applied to identify the best background subtraction algorithm, to optimize the background subtractor parameters and to calculate the optimal number of pre- and post-processing operations. The results show that the best algorithm for fall-detection in indoor, night-time environments is the LBAdaptativeSOM, optimal parameters and processing operations for this algorithm are reported.

Funding Projects

TypeCodeAcronymLeaderTitle
Madrid Regional GovernmentS2018/NMT-4331RoboCity2030-DIH-CMUnspecifiedUnspecified

More information

Item ID: 57108
DC Identifier: http://oa.upm.es/57108/
OAI Identifier: oai:oa.upm.es:57108
DOI: 10.1109/ACCESS.2019.2948321
Official URL: https://ieeexplore.ieee.org/document/8876591
Deposited by: Memoria Investigacion
Deposited on: 29 Oct 2019 15:52
Last Modified: 29 Oct 2019 15:52
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