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Visual saliency based object tracking

  • Xi'an Jiaotong University
  • IBM China Research Lab

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

36 Scopus citations

Abstract

This paper presents a novel method of on-line object tracking with the static and motion saliency features extracted from the video frames locally, regionally and globally. When detecting the salient object, the saliency features are effectively combined in Conditional Random Field (CRF). Then Particle Filter is used when tracking the detected object. Like the attention shifting mechanism of human vision, when the object being tracked disappears, our tracking algorithm can change its target to other object automatically even without re-detection. And different from many other existing tracking methods, our algorithm has little dependence on the surface appearance of the object, so it can detect any category of objects as long as they are salient, and the tracking is robust to the change of global illumination and object shape. Experiments on video clips of various objects show the reliable results of our algorithm.

Original languageEnglish
Title of host publicationComputer Vision, ACCV 2009 - 9th Asian Conference on Computer Vision, Revised Selected Papers
Pages193-203
Number of pages11
EditionPART 2
DOIs
StatePublished - 2010
Event9th Asian Conference on Computer Vision, ACCV 2009 - Xi'an, China
Duration: 23 Sep 200927 Sep 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume5995 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th Asian Conference on Computer Vision, ACCV 2009
Country/TerritoryChina
CityXi'an
Period23/09/0927/09/09

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