Institut for Forretningsudvikling og Teknologi

Ramjee Prasad

Improved Gaussian Mixture Models for Adaptive Foreground Segmentation

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DOI

  • Nikolaos Katsarakis, Danmark
  • Aristodemos Pnevmatikakis, Grækenland
  • Zheng-Hua Tan, Danmark
  • Ramjee Prasad
Adaptive foreground segmentation is traditionally performed using Stauffer & Grimson’s algorithm that models every pixel of the frame by a mixture of Gaussian distributions with continuously adapted parameters. In this paper we provide an enhancement of the algorithm by adding two important dynamic elements to the baseline algorithm: The learning rate can change across space and time, while the Gaussian distributions can be merged together if they become similar due to their adaptation process. We quantify the importance of our enhancements and the effect of parameter tuning using an annotated outdoors sequence.
OriginalsprogEngelsk
TidsskriftWireless Personal Communications
Vol/bind87
Nummer3
Sider (fra-til)629-643
Antal sider14
ISSN0929-6212
DOI
StatusUdgivet - apr. 2016
Eksternt udgivetJa

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