Gait shape estimation for identification

David Tolliver, Robert Collins

Research output: Contribution to journalArticle

33 Citations (Scopus)

Abstract

A method is presented for identifying individuals by shape, given a sequence of noisy silhouettes segmented from video. A spectral partitioning framework is used to cluster similar poses and automatically extract gait shapes. The method uses a variance-weighted similarity metric to induce clusters that cover disparate stages in the gait cycle. This technique is applied to the HumanID Gait Challenge dataset to measure the quality of the shape model, and the efficacy of shape statistics in human identification.

Original languageEnglish (US)
Pages (from-to)734-742
Number of pages9
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2688
StatePublished - Dec 1 2003

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Gait
Statistics
Silhouette
Efficacy
Partitioning
Cover
Cycle
Metric
Model

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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abstract = "A method is presented for identifying individuals by shape, given a sequence of noisy silhouettes segmented from video. A spectral partitioning framework is used to cluster similar poses and automatically extract gait shapes. The method uses a variance-weighted similarity metric to induce clusters that cover disparate stages in the gait cycle. This technique is applied to the HumanID Gait Challenge dataset to measure the quality of the shape model, and the efficacy of shape statistics in human identification.",
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