Monte Carlo simulation techniques for probabilistic tracking

Baoxin Li, Rama Chellappa, Hankyu Moon

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

1 Scopus citations


In this paper, two novel approaches to probabilistic tracking using Monte Carlo simulation are presented. The first approach is a 3D shape encoded object tracking algorithm. The measurements are derived using the outputs of shape-encoded filters. The nonlinear state estimation is performed by solving the Zakai equation, and we use the branching particle propagation method for computing the solution. The second approach is an algorithm for simultaneous tracking and verification in video data. The approach is based on posterior density estimation using sequential Monte Carlo methods. Visual tracking, which is in essence a temporal correspondence problem, is solved through probability density propagation, with the density being defined over a proper state space characterizing the object configuration. Verification is realized through hypothesis testing using the estimated posterior density. Several applications of both approaches including human head and body tracking, human identification and facial feature based face verification are illustrated by experiments devised to evaluate their performance.

Original languageEnglish (US)
Title of host publicationConference Record of the Asilomar Conference on Signals, Systems and Computers
EditorsM.B. Matthews
Number of pages8
StatePublished - 2001
Externally publishedYes
Event35th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States
Duration: Nov 4 2001Nov 7 2001


Other35th Asilomar Conference on Signals, Systems and Computers
Country/TerritoryUnited States
CityPacific Grove, CA

ASJC Scopus subject areas

  • Hardware and Architecture
  • Signal Processing
  • Electrical and Electronic Engineering


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