An online motion-based particle filter for head tracking applications

Nidhal Bouaynaya, Wei Qu, Dan Schonfeld

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

24 Scopus citations

Abstract

Particle filtering framework has revolutionized probabilistic tracking of objects in a video sequence. In this framework the proposal density can be any density as long as its support includes that of the posterior. However, in practice, the number of samples is finite and consequently the choice of the proposal is crucial to the effectiveness of the tracking. The CONDENSATION filter uses the transition prior as the proposal density. We propose in this paper a motion-based proposal. We use Adaptive Block Matching (ABM) as the motion estimation technique. The benefits of this model are two fold. It increases the sampling efficiency and handles abrupt motion changes. Analytically, we derive a Kullback-Leibler (KL)-based performance measure and show that the motion proposal is superior to the proposal of the CONDENSATION filter. Our experiments are applied to head tracking. Finally, we report promising tracking results in complex environments.

Original languageEnglish (US)
Title of host publication2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Proceedings - Image and Multidimensional Signal Processing Multimedia Signal Processing
PagesII225-II228
DOIs
StatePublished - 2005
Externally publishedYes
Event2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Philadelphia, PA, United States
Duration: Mar 18 2005Mar 23 2005

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
VolumeII
ISSN (Print)1520-6149

Other

Other2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05
CountryUnited States
CityPhiladelphia, PA
Period3/18/053/23/05

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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