NDLI: Background subtraction in dynamic scenes using the dynamic principal component analysis

Content Provider	IET Digital Library
Author	Djerida, Achraf Zhao, Zhonghua Zhao, Jiankang
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.
Starting Page	245
Ending Page	255
Page Count	11
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 2, Feb (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.6095
Journal	IET Image Processing
Publisher Date	2019-10-28
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Background Subtraction Background Time-lagged Data Matrix Computer Vision And Image Processing Technique Corresponding Detection Thresholds Depth-based Method Dynamic Effects Dynamic Principal Component Analysis Dynamic Scenes Estimation Theory Foreground Detection Method Gaussian Processes Image Colour Analysis Image Motion Analysis Image Sequence Kernel Density Estimation Object Detection Optical, Image And Video Signal Processing Principal Component Analysis Robust Pixel-based Background Model Statistics Successive Frames Value Colour Space Video Signal Processing
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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