A New Global Foreground Modeling and Local Background Modeling Method for Video Analysis

Hang Shi, Chengjun Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

This paper presents a new Global Foreground Modeling (GFM) and Local Background Modeling (LBM) method for video analysis. First, a novel feature vector, which integrates the RGB values, the horizontal and vertical Haar wavelet features, and the temporal difference features of a pixel, enhances the discriminatory power due to its increased dimensionality. Second, the local background modeling process chooses the most significant single Gaussian density to model the background locally for each pixel according to the weights learned for the Gaussian mixture model. Third, an innovative global foreground modeling method, which applies the Bayes decision rule, models the foreground pixels globally. The GFM method thus is able to achieve improved foreground detection accuracy and capable of detecting stopped moving objects. Experimental results using the New Jersey Department of Transportation (NJDOT) traffic video sequences show that the proposed method achieves better video analysis results than the popular background subtraction methods.

Original languageEnglish (US)
Title of host publicationMachine Learning and Data Mining in Pattern Recognition - 14th International Conference, MLDM 2018, Proceedings
EditorsPetra Perner
PublisherSpringer Verlag
Pages49-63
Number of pages15
ISBN (Print)9783319961354
DOIs
StatePublished - Jan 1 2018
Event14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018 - New York, United States
Duration: Jul 15 2018Jul 19 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10934 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018
CountryUnited States
CityNew York
Period7/15/187/19/18

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Background subtraction
  • Bayes decision rule
  • Gaussian mixture model
  • Global Foreground Modeling (GFM)
  • Local Background Modeling (LBM)
  • Video analysis

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