Adaptive mean shift for target- tracking in FLIR imagery

Yin Yafeng, Man Hong

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

Abstract

In this paper, we present a novel adaptive mean-shift tracker for tracking moving targets in the FLIR imagery, captured from an airborne moving platform. First, each target's position is manually marked at the first frame to initialize the adaptive mean-shift based tracker. For each target, multiple different features are extracted from both the targets and background during tracking, and an on-line feature ranking method is deployed to adaptively select the most discriminative feature for the mean-shift iteration. In addition, to compensate the motion of the moving platform, a block matching method is applied to compute the motion vector, which will be used in the RANSAC algorithm to estimate the affine model for global motion. We test our method on the AMCOM FLIR data set, the results indicate that our Adaptive mean-shift tracker can track each target accurately and robustly.

Original languageEnglish
Title of host publicationWOCC 2009 - 18th Annual Wireless and Optical Communications Conference
DOIs
StatePublished - 2009
EventWOCC 2009 - 18th Annual Wireless and Optical Communications Conference - Newark, NJ, United States
Duration: May 1 2009May 2 2009

Publication series

NameWOCC 2009 - 18th Annual Wireless and Optical Communications Conference

Conference

ConferenceWOCC 2009 - 18th Annual Wireless and Optical Communications Conference
Country/TerritoryUnited States
CityNewark, NJ
Period5/1/095/2/09

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering
  • Communication

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