Dynamic Spatial Predicted Background for Video Surveillance

Yaniv Tocker, Rami R. Hagege, Joseph M. Francos

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Video foreground-background separation is considered as a basic step for many computer vision applications. Common approaches excel in handling background variances while trying to keep the computational load low. We propose a novel method that models the scene as a superposition of illumination effects while predicting each pixel's value with a linear estimator comprised by a few other pixels of the scene. By doing so, we are able to achieve real-time performance using minimal hardware, which is a crucial consideration for embedding such a system on surveillance cameras. Experimental results on two common datasets show our method's potential by comparing it to state-of-the-art methods.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
PublisherIEEE Computer Society
Pages4005-4009
Number of pages5
ISBN (Electronic)9781538662496
DOIs
StatePublished - 1 Sep 2019
Event26th IEEE International Conference on Image Processing, ICIP 2019 - Taipei, Taiwan, Province of China
Duration: 22 Sep 201925 Sep 2019

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2019-September
ISSN (Print)1522-4880

Conference

Conference26th IEEE International Conference on Image Processing, ICIP 2019
Country/TerritoryTaiwan, Province of China
CityTaipei
Period22/09/1925/09/19

Keywords

  • Background Modeling
  • Computer Vision
  • Motion Detection
  • Video Analysis

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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