Gait shape estimation for identification

David Tolliver, Robert T. Collins

Research output: Chapter in Book/Report/Conference proceedingChapter

33 Scopus citations

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)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsJosef Kittler, Mark S. Nixon
PublisherSpringer Verlag
Pages734-742
Number of pages9
ISBN (Electronic)9783540403029
DOIs
StatePublished - 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2688
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Gait shape estimation for identification'. Together they form a unique fingerprint.

Cite this