Spatio-temporal background models for object detection

Satoshi Yoshinaga, Yosuke Nonaka, Atsushi Shimada, Hajime Nagahara, Rin Ichiro Taniguchi

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

One of the fundamental problems in computer vision is detecting regions or objects of interest from an image sequence. Background subtraction, which removes a background image from the input image, is widely used for detecting foreground objects in practical applications, since it enables us to detect foreground objects without any previous knowledge of them. However, simple background subtraction often detects not only foreground objects but also a lot of noise regions, because it is quite sensitive to background changes. In general, background changes which occur in outdoor scenes can be mainly classified into two types: • Illumination changes – changes caused by lighting conditions such as the sun rising, setting, or being blocked by clouds, • Dynamic changes – changes caused by the swaying motion of tree branches, leaves and grass, fleeting cloud, waves on water and so on.

Original languageEnglish
Title of host publicationBackground Modeling and Foreground Detection for Video Surveillance
PublisherCRC Press
Pages13-1-13-20
ISBN (Electronic)9781482205381
ISBN (Print)9781482205374
DOIs
Publication statusPublished - Jan 1 2014

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Engineering(all)
  • Mathematics(all)

Fingerprint Dive into the research topics of 'Spatio-temporal background models for object detection'. Together they form a unique fingerprint.

Cite this