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A GPU-Based Statistical Framework for Moving Object Segmentation: Implementation, Analysis and Applications

CUZZOCREA, Alfredo Massimiliano
•
MUMOLO, ENZO
•
Moro, Alessandro
•
Umeda, Kazunori
2015
  • conference object

Abstract
This paper describes a real-time implementation of a recently proposed background maintenance algorithm and reports the relative performances. Experimental results on dynamic scenes taken from a xed camera show that the proposed parallel algorithm produces background images with an improved quality with respect to classical pixel-wise algorithms, obtaining a speedup of more than 35 times compared to CPU implementation. It is worth noting that we used both the GeForce 9 series (actually a 9800 GPU) available from the year 2008 and the GeForce 200 series (actually a 295 GPU) available from the year 2009. Finally, we show that this parallel implementation allows us to use it in real-time moving object detection application.
DOI
10.1007/978-3-319-23237-9
WOS
WOS:000364687700019
Archivio
http://hdl.handle.net/11368/2857453
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-84945562386
http://www.springer.com/la/book/9783319232362
Diritti
metadata only access
Soggetti
  • Moving Object Segment...

  • GPU computing

  • real-time implementat...

  • background maintenanc...

Scopus© citazioni
0
Data di acquisizione
Jun 7, 2022
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Visualizzazioni
2
Data di acquisizione
Apr 19, 2024
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