Open AccessBackground subtraction in dynamic scenes using the dynamic principal component analysis
Author(s) -
Djerida Achraf,
Zhao Zhonghua,
Zhao Jiankang
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.6095
Subject(s) - background subtraction , principal component analysis , foreground detection , artificial intelligence , computer science , kernel density estimation , pattern recognition (psychology) , hue , pixel , kernel (algebra) , kernel principal component analysis , computer vision , robust principal component analysis , mathematics , statistics , kernel method , support vector machine , combinatorics , estimator
This study presents a foreground detection method capable of robustly estimating the background under the presence of dynamic effects. The key contribution of this study is the use of the dynamic principal component analysis to model the serial correlation between successive frames and construct a robust pixel‐based background model. The frames are normalised in hue, saturation and value colour space to reduce the effect of illumination changes. To restrict the background model, kernel density estimation is used to identify the distribution of the background time‐lagged data matrix and then confidence interval limits are used to determine the corresponding detection thresholds. The foreground is detected using background subtraction. This method is tested on several common sequences such as CDnet 2014, ETSI 2014 and MULTIVISION 2013. The authors also hold comparisons based on quantitative metrics with several state‐of‐the‐art methods. Experimental results show that their method outperforms some state‐of‐the‐art methods and has comparable performance with some depth‐based methods.