Using localized particle subset for target tracking in videos

Lei Ma, Jennie Si, Glen P. Abousleman

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

Abstract

In this paper, a localized particle subset method is proposed to solve target tracking problem in a Bayesian inference framework. Instead of using all particles to estimated the posterior probability density function (pdf) of targets, a subset is used. This subset of particles is selected by estimated motion of the targets. The weights of particles are updated by the 3D Hausdroff distances between target appearance model and samples. The proposed method is highly efficient in utilizing the particles, which consequently results in reduction of samples utilized in the prediction and update processes. It is also able to alleviate the sample degeneracy and impoverishment problems in the sampling process. Experiments show that the computation complexity for localized particle subset tracker is reduce to a fraction of that of the Sequential Importance (SIS) tracker but with compatible performance.

Original languageEnglish (US)
Title of host publicationSignal Processing, Sensor Fusion, and Target Recognition XV
DOIs
StatePublished - Sep 20 2006
EventSignal Processing, Sensor Fusion, and Target Recognition XV - Kissimmee, FL, United States
Duration: Apr 17 2006Apr 19 2006

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume6235
ISSN (Print)0277-786X

Other

OtherSignal Processing, Sensor Fusion, and Target Recognition XV
CountryUnited States
CityKissimmee, FL
Period4/17/064/19/06

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Keywords

  • Motion estimation
  • Particle filter
  • Target tracking

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

Ma, L., Si, J., & Abousleman, G. P. (2006). Using localized particle subset for target tracking in videos. In Signal Processing, Sensor Fusion, and Target Recognition XV [623507] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 6235). https://doi.org/10.1117/12.665823