Multi objective optimization based fast motion detector

Jia Su, Xin Wei, Xiaocong Jin, Takeshi Ikenaga

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

1 Citation (Scopus)

Abstract

A large number of surveillance applications require fast action, and since many surveillance applications, motive objects contain most critical information. Fast detection algorithm system becomes a necessity. A problem in computer vision is the determination of weights for multiple objective function optimizations. In this paper we propose techniques for automatically determining the weights, and discuss their properties. The Min-Max Principle, which avoids the problems of extremely low or high weights, is introduced. Expressions are derived relating the optimal weights, objective function values, and total cost. Simulation results show, compared to the conventional work, it can achieve around 40% time saving and higher detection accuracy for both outdoor and indoor surveillance videos.

Original languageEnglish
Title of host publicationAdvances in Multimedia Modeling - 17th International Multimedia Modeling Conference, MMM 2011, Proceedings
Pages492-502
Number of pages11
EditionPART 1
DOIs
Publication statusPublished - 2011 Jan 26
Event17th Multimedia Modeling Conference, MMM 2011 - Taipei, Taiwan, Province of China
Duration: 2011 Jan 52011 Jan 7

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6523 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th Multimedia Modeling Conference, MMM 2011
CountryTaiwan, Province of China
CityTaipei
Period11/1/511/1/7

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Keywords

  • Motion detection
  • Multi-objective optimization
  • Video surveillance

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Su, J., Wei, X., Jin, X., & Ikenaga, T. (2011). Multi objective optimization based fast motion detector. In Advances in Multimedia Modeling - 17th International Multimedia Modeling Conference, MMM 2011, Proceedings (PART 1 ed., pp. 492-502). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6523 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-17832-0_46