Object tracking under sensing lighting equipments

Cheng Hsiang Chiu*, Pang Chan Hung, Hsing Lu Huang, Jen-Hui Chuang

*Corresponding author for this work

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

1 Scopus citations

Abstract

In this paper, image processing techniques are applied to the analysis of near-infrared videos. The goal is to detect human activities in the videos. For detecting human activities, we implement the Gaussian mixture modeling (GMM) to construct background model and to perform foreground detection. Additionally, we pay attention to commonly used sensing lighting equipments used in nighttime environment because of its illumination and shadowing phenomena. Accordingly, a two-mode GMM is proposed which separately constructs background GMM for different lighting conditions and switches GMM modes by event detection. In order to cope with excessive shadowing phenomenon, an efficient way of searching footholds by using scan-lines is proposed to remove human shadows. The proposed approach will provide reasonable bounding boxes information of human regions as detection results which will be very helpful for a nighttime surveillance system.

Original languageEnglish
Title of host publicationProceedings of the 2010 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010
Pages2296-2299
Number of pages4
DOIs
StatePublished - 1 Sep 2010
Event5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010 - Taichung, Taiwan
Duration: 15 Jun 201017 Jun 2010

Publication series

NameProceedings of the 2010 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010

Conference

Conference5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010
CountryTaiwan
CityTaichung
Period15/06/1017/06/10

Keywords

  • Gaussian mixture modeling
  • Near-infrared
  • Sensing lighting equipments
  • Two-mode GMM

Fingerprint Dive into the research topics of 'Object tracking under sensing lighting equipments'. Together they form a unique fingerprint.

Cite this