Conditional Random People: Tracking Humans with CRFs and Grid Filters

Abstract

We describe a state-space tracking approach based on a Conditional Random Field (CRF) model, where the observation potentials are learned from data. We find functions that embed both state and observation into a space where similarity corresponds to L1 distance, and define an observation potential based on distance in this space. This potential is extremely fast to compute and in conjunction with a grid-filtering framework can be used to reduce a continuous state estimation problem to a discrete one. We show how a state temporal prior in the grid-filter can be computed in a manner similar to a sparse HMM, resulting in real-time system performance. The resulting system is used for human pose tracking in video sequences.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2005
Accession Number
ADA466726

Entities

People

  • David Demirdjian
  • Gregory Shakhnarovich
  • Leonid Taycher
  • Trevor Darrell

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Computations
  • Computer Science
  • Computer Vision
  • Databases
  • Estimators
  • Filters
  • Filtration
  • Generative Models
  • Grids
  • Information Science
  • Kalman Filters
  • Probability
  • Standards
  • Statistical Analysis
  • Test And Evaluation

Fields of Study

  • Computer science
  • Physics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Space
  • Space - Space Objects