Fast Moving Objects Detection Using iLBP Background Model

Abstract

ABSTRACT:In this paper a new approach for moving objects detection in video surveillance systems is proposed. It is based on iLBP (intensity local binary patterns) descriptor that combines the classic LBP (local binary patterns) and the multiple regressive pseudospectra model. The iLBP descriptor itself is considered together with computational algorithm that is based on the sign image representation. We show that motion analysis methods based on iLBP allow uniformly detecting objects that move with different speed or even stop for a short while along with unattended objects. We also show that proposed model is comparable to the most popular modern background models, but is significantly faster.

An eXtended Center-Symmetric Local Binary Pattern for Background Modeling and Subtraction in Videos

“…Because of this, there could be a wrong pixel comparison result when intensity values of pixels differ drastically, but their LBP values are identical. To overcome this drawback, Vishnyakov et al (2014) propose an intensity LBP (iLBP) to build a fast background model is proposed in (Vishnyakov et al, 2014). It is defined as a collection of LBP descriptor values and intensity values of the image.…”

“…Finally, concluding remarks and some perspectives are drawn in Section 5. (Ojala et al, 2002) • 256 Modified LBP (Heikkilä and Pietikäinen, 2006) • • 256 CS-LBP (Heikkilä et al, 2009) • 16 STLBP (Shimada and Taniguchi, 2009) • • 256 εLBP • 256 Adaptive εLBP • 256 SCS-LBP (Xue et al, 2010) • • 16 SILTP (Liao et al, 2010) • 256 CS-LDP (Xue et al, 2011) • 16 SCBP (Xue et al, 2011) • 64 OCLBP (Lee et al, 2011) • 1536 Uniform LBP (Yuan et al, 2012) • 59 SALBP (Noh and Jeon, 2012) • 128 SLBP-AM (Yin et al, 2013) • • 256 LBSP (Bilodeau et al, 2013) • • 256 iLBP (Vishnyakov et al, 2014) • 256 CS-SILTP (Wu et al, 2014) • • 16 XCS-LBP (in this paper)…”

Vehicles detection for illumination changes urban traffic scenes employing adaptive local texture feature background model

“…Shimada et al [17] improved LBP operator to acquire Spatial‐Temporal LBP (STLBP) and handle long‐term or short‐term illumination changes employing a new hybrid background model, and then the STLBP was introduced to extract the coarse moving objects in the H.264 compression domain [18]. LBP [19] and i LBP [20] operators improve the traditional LBP operator and are applied to extract the local description of each pixel. Combining the strengths of robust illumination normalisation, the Local Ternary Pattern (LTP) operator [21], which is more discriminant and less sensitive to noise in uniform regions, was presented to eliminate most of the effects of changing illumination.…”