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A two-stage object tracking method based on Curvelet transform and mean shift algorithm

  • Pengcheng Han
  • , Junping Du
  • , Qingping Li
  • , Ming Fang
  • , Yuehua Yang
  • , Yingmin Jia
  • Beijing University of Posts and Telecommunications

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

Abstract

Traditional mean shift tracking algorithm couldn't track moving objects in cross-scale domain. In this paper, we propose a new two-stage object tracking method combined Curvelet Transform and mean shift algorithm. Our proposed method extracts image features using Curvelet transform, and calculates object location by cross-scale mean shift algorithm. The experimental results demonstrate that the proposed algorithm can effectively track moving objects. Compared with traditional mean shift algorithm, tracking accuracy has been significantly improved.

Original languageEnglish
Title of host publication2013 IEEE International Symposium on Industrial Electronics, ISIE 2013
DOIs
StatePublished - 2013
Event2013 IEEE 22nd International Symposium on Industrial Electronics, ISIE 2013 - Taipei, Taiwan, Province of China
Duration: 28 May 201331 May 2013

Publication series

NameIEEE International Symposium on Industrial Electronics

Conference

Conference2013 IEEE 22nd International Symposium on Industrial Electronics, ISIE 2013
Country/TerritoryTaiwan, Province of China
CityTaipei
Period28/05/1331/05/13

Keywords

  • Curvelet transform
  • mean shift
  • object tracking
  • translation Invariant

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