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Tracking a walking person using activity-guided annealed particle filtering

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conference contribution
posted on 09.02.2016, 14:35 authored by John Darby, Baihua Li, Nicholas Costen
Tracking human pose using observations from less than three cameras is a challenging task due to ambiguity in the available image evidence. This work presents a method for tracking using a pre-trained model of activity to guidesampling within an Annealed Particle Filtering framework. The approach is an example of model-based analysis-by-synthesis and is capable of robust tracking from less than 3 cameras with reduced numbers of samples. We test the scheme on a common dataset containing ground truth mo-tion capture data and compare against quantitative results for standard Annealed Particle Filtering. We find lower ab-solute and relative error scores for both monocular and 2-camera sequences using 80% fewer particles. © 2008 IEEE.

History

School

  • Science

Department

  • Computer Science

Published in

2008 8th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2008

Citation

DARBY, J., LI, B. and COSTEN, N., 2008. Tracking a walking person using activity-guided annealed particle filtering. 8th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2008, Amsterdam, 17th-19th September, 6pp.

Publisher

© IEEE

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2008

Notes

This is the accepted manuscript version of the paper. © 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

ISBN

9781424421541;9781424421534

Language

en

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