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

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. Th...

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Bibliographic Details
Published in:IEEE access Vol. 7; pp. 152399 - 152411
Main Authors: Alonso, Mercedes, Brunete, Alberto, Hernando, Miguel, Gambao, Ernesto
Format: Journal Article
Language:English
Published: Piscataway IEEE 2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptation models
Algorithms
background-subtraction
camera-based
Cameras
Detectors
Fall detection
Fuzzy logic
Genetic algorithms
Indoor environments
Night
Optimization
Parameters
Post-processing
Senior citizens
Subtraction
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary: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.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2948321