Extended scale invariant local binary pattern for background subtraction. Issue 8 (1st August 2018)

• Record Type: Journal Article
• Authors: Zeng, Dongdong; Zhu, Ming; Xu, Fang; Zhou, Tongxue
• Is Part Of: IET image processing. Volume 12:Issue 8(2018)
• Page Start: 1292
• Page End: 1302
• Publication Date: 2018-08-01
• Subjects: object detection -- image segmentation -- video surveillance -- feature extraction -- image colour analysis -- image texture; extended scale invariant local binary pattern -- local texture features -- rich texture regions -- photometric invariant colour measurement -- well‐known change detection dataset -- video surveillance systems -- effective background subtraction algorithm -- background sensitively -- background scenes -- robust background subtraction algorithm; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2016.1026
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Background subtraction based on change detection is the first step in many video surveillance systems, an effective background subtraction algorithm should distinguish foreground from the background sensitively, and adapt to the variation of background scenes robustly. In this study, the authors propose a robust background subtraction algorithm which takes advantages of local texture features represented by an extended scale invariant local binary pattern and colour intensities to characterise pixel representations. Local texture features achieve good tolerance against illumination variations in rich texture regions but not so efficiently on uniform regions, so a photometric invariant colour measurement is proposed to overcome its limitation. Both quantitative and qualitative evaluations carried out on a well‐known change detection dataset are provided to demonstrate the effectiveness of the proposed algorithm.