TY  - JOUR
JF  - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
A1  - Hassan, M.A.
A1  - Malik, A.S.
A1  - Nicolas, W.
A1  - Faye, I.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958520461&doi=10.1007%2f978-3-319-16631-5_29&partnerID=40&md5=2ac3541f910686ae3720426c9ca9e5c0
VL  - 9009
Y1  - 2015///
N2  - Background modeling is one of the key steps in any visual surveillance system. A good background modeling algorithm should be able to detect objects/targets under any environmental condition. The influence of illumination variance has been a major challenge in many background modeling algorithms. These algorithms produce poor object segmentation or consume substantial amount of computational time, which makes them not implementable at real time. In this paper we propose a novel background modeling method based on Gaussian Mixture Method (GMM). The proposed method uses Phase Congruency (PC) edge features to overcome the effect of illumination variance, while preserving efficient background/foreground segmentation. Moreover, our method uses a combination of pixel information of GMM and the Phase texture information of PC, to construct a foreground invariant of the illumination variance. © Springer International Publishing Switzerland 2015.
ID  - scholars6152
KW  - Algorithms; Computer vision; Image segmentation
KW  -  Computational time; Environmental conditions; Foreground extraction; Gaussian mixture methods; Object segmentation; Texture information; Unconstrained environments; Visual surveillance systems
KW  -  Object detection
EP  - 400
PB  - Springer Verlag
SN  - 03029743
N1  - cited By 6; Conference of 12th Asian Conference on Computer Vision, ACCV 2014 ; Conference Date: 1 November 2014 Through 2 November 2014; Conference Code:142259
TI  - Adaptive foreground extraction for crowd analytics surveillance on unconstrained environments
SP  - 390
AV  - none
ER  -