Background subtraction in dynamic scenes using the dynamic principal component analysis. Issue 2 (1st February 2020)

• Record Type: Journal Article
• Authors: Djerida, Achraf; Zhao, Zhonghua; Zhao, Jiankang
• Is Part Of: IET image processing. Volume 14:Issue 2(2020)
• Page Start: 245
• Page End: 255
• Publication Date: 2020-02-01
• Subjects: estimation theory -- image colour analysis -- object detection -- principal component analysis -- image motion analysis -- image sequences -- video signal processing -- Gaussian processes; corresponding detection thresholds -- background subtraction -- depth‐based methods -- dynamic scenes -- dynamic principal component analysis -- foreground detection method -- dynamic effects -- successive frames -- robust pixel‐based background model -- value colour space -- kernel density estimation -- background time‐lagged data matrix; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.6095
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : 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.