Light-weight salient foreground detection with adaptive memory requirement

Mauricio Casares, Senem Velipasalar

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

4 Scopus citations

Abstract

Designing algorithms, which require less memory and consume less power, is very important for the portability to embedded smart cameras, which have limited resources. We present a light-weight and efficient algorithm for salient foreground detection that is highly robust against lighting variations and non-static backgrounds such as scenes with swaying trees. Contrary to traditional methods, memory requirement for the data saved for each pixel is very small in the proposed algorithm. Moreover, the total memory requirement is adaptive, and is decreased even more depending on the amount of activity in the scene. As opposed to existing methods, we treat each pixel differently based on its history. Instead of requiring the same amount of memory for every pixel, we allocate less memory for stable background pixels. The plot of the required memory at each frame also serves as a tool to find the video portions with high activity.

Original languageEnglish (US)
Title of host publication2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009
Pages1245-1248
Number of pages4
DOIs
StatePublished - Sep 23 2009
Externally publishedYes
Event2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009 - Taipei, Taiwan, Province of China
Duration: Apr 19 2009Apr 24 2009

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009
CountryTaiwan, Province of China
CityTaipei
Period4/19/094/24/09

Keywords

  • Background subtraction
  • Foreground detection
  • Light-weight algorithm
  • Memory
  • Salient motion

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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