A new approach of GPU accelerated visual tracking

Chuantao Zang, Koichi Hashimoto

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

1 Citation (Scopus)


In this paper a fast and robust visual tracking approach based on GPU acceleration is proposed. It is an effective combination of two GPU-accelerated algorithms. One is a GPU accelerated visual tracking algorithm based on the Efficient Second-order Minimization (GPU-ESM) algorithm. The other is a GPU based Scale Invariant Feature Transform (SIFT) algorithm, which is used in those extreme cases for GPU-ESM tracking algorithm, i.e. large image differences, occlusions etc. System performances have been greatly improved by our combination approach. We have extended the tracking region from a planar region to a 3D region. Translation details of both GPU algorithms and their combination strategy are described. System performances are evaluated with experimental data. Optimization techniques are presented as a reference for GPU application developers.

Original languageEnglish
Title of host publicationAdvanced Concepts for Intelligent Vision Systems - 12th International Conference, ACIVS 2010, Proceedings
Number of pages12
EditionPART 2
Publication statusPublished - 2010
Event12th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2010 - Sydney, NSW, Australia
Duration: 2010 Dec 132010 Dec 16

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6475 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference12th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2010
CitySydney, NSW


  • ESM
  • GPU
  • SIFT
  • Visual tracking


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