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Visual object tracking based on particle filter re-detection

  • Di Yuan
  • , Guanglei Zhao
  • , Donghao Li
  • , Zhenyu He
  • , Nan Luo*
  • *Corresponding author for this work
  • School of Computer Science and Technology, Harbin Institute of Technology
  • Guizhou Finance School
  • Heilongjiang Academy of Sciences

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

Abstract

The accurate localization of a object target is a challenging research issue in visual tracking. Most correlation filter based tracking algorithms has been degraded their performances because of the weaknesses of their search strategy. This paper investigates the problem of accurate location the object target in visual tracking sequences. We propose a novel particle filter re-detection tracking approach for target re-location, when the kernelized correlation filters tracking result becomes unreliable. Additionally, we give a new target scale evaluation. Different from other proposed scale search strategies, our method merely consider the difference between the maximum value of the response map of adjacent frames. Extensive experiments are performed on the OTB2013 dataset. On the result of this benchmark, the proposed approach achieves a pretty performance.

Original languageEnglish
Title of host publication2017 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7-12
Number of pages6
ISBN (Electronic)9781538630167
DOIs
StatePublished - 2 Jul 2017
Externally publishedYes
Event2017 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2017 - Shenzhen, China
Duration: 15 Dec 201717 Dec 2017

Publication series

Name2017 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2017
Volume2018-January

Conference

Conference2017 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2017
Country/TerritoryChina
CityShenzhen
Period15/12/1717/12/17

Keywords

  • Visual object tracking
  • correlation filter
  • particle filter re-detection
  • scale evaluation

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