Particle filter with gaussian weighting for human tracking

Indah Agustien Siradjuddin, Muhammad Rahmat Widyanto, T. Basaruddin

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

Particle filter for object tracking could achieve high tracking accuracy. To track the object, this method generates a number of particles which is the representation of the candidate target object. The location of target object is determined by particles and each weight. The disadvantage of conventional particle filter is the computational time especially on the computation of particle's weight. Particle filter with Gaussian weighting is proposed to accomplish the computational problem. There are two main stages in this method, i.e. prediction and update. The difference between the conventional particle filter and particle filter with Gaussian weighting is in the update Stage. In the conventional particle filter method, the weight is calculated in each particle, meanwhile in the proposed method, only certain particle's weight is calculated, and the remain particle's weight is calculated using the Gaussian weighting. Experiment is done using artificial dataset. The average accuracy is 80,862%. The high accuracy that is achieved by this method could use for the real-time system tracking.

Original languageEnglish
Pages (from-to)801-806
Number of pages6
JournalTelkomnika
Volume10
Issue number4
DOIs
Publication statusPublished - 1 Jan 2012

Keywords

  • Bayesian
  • Particle filter
  • Prediction
  • Update

Fingerprint Dive into the research topics of 'Particle filter with gaussian weighting for human tracking'. Together they form a unique fingerprint.

Cite this