A modular approach to the analysis and evaluation of particle filters for figure tracking

Wang Ping, James M. Rehg

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents the first systematic empirical study of the particle filter (PF) algorithms for human figure tracking in video. Our analysis and evaluation follows a modular approach which is based upon the underlying statistical principles and computational concerns that govern the performance of PF algorithms. Based on our analysis, we propose a novel PF algorithm for figure tracking with superior performance called the Optimized Unscented PF. We examine the role of edge and template features, introduce computationally- equivalent sample sets, and describe a method for the automatic acquisition of reference data using standard motion capture hardware. The software and test data are made publicly-available on our project website.

Original languageEnglish (US)
Title of host publicationProceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
Pages790-797
Number of pages8
DOIs
StatePublished - 2006
Externally publishedYes
Event2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006 - New York, NY, United States
Duration: Jun 17 2006Jun 22 2006

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume1
ISSN (Print)1063-6919

Other

Other2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
Country/TerritoryUnited States
CityNew York, NY
Period6/17/066/22/06

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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