Open AccessImproved GMM‐based method for target detection
Author(s) -
Zhao Juan,
Xie Guihui,
Li Dongming,
Song Mailing
Publication year - 2020
Publication title -
iet networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.466
H-Index - 21
ISSN - 2047-4962
DOI - 10.1049/iet-net.2019.0038
Subject(s) - artificial intelligence , computer science , pattern recognition (psychology) , chromatography , chemistry
The Gaussian mixture model (GMM) is prone to large‐scale misdetection in the static case where the background and foreground have similar colours. This study presents an improved GMM method to solve this problem. First, the principal component analysis is used to transform the high‐dimensional space into the low‐dimensional one with three colour channels, which aims to reduce runtime. Then, the images are processed by GMM to obtain the foreground areas. At the same time, the mean and difference of pixel features in red, green and blue and hue, saturation and value (HSV) colour models are calculated to analyse the similar‐coloured components. The areas with similar colours are classified in this process, and thus the foreground is separated from similar‐coloured background. Then, all of the extracted areas are targets. The experimental results show that the presented method identified the target areas with high accuracy and high efficiency. Compared with conventional GMM, the presented method obtained the detection‐accuracy improvement by about 6% on average.