Gait sequence analysis using frieze patterns

Yanxi Liu, Robert Collins, Yanghai Tsin

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

88 Citations (Scopus)

Abstract

We analyze walking people using a gait sequence representation that bypasses the need for frame-to-frame tracking of body parts. The gait representation maps a video sequence of silhouettes into a pair of two-dimensional spatio-temporal patterns that are near-periodic along the time axis. Mathematically, such patterns are called “frieze” patterns and associated symmetry groups “frieze groups”. With the help of a walking humanoid avatar, we explore variation in gait frieze patterns with respect to viewing angle, and find that the frieze groups of the gait patterns and their canonical tiles enable us to estimate viewing direction of human walking videos. In addition, analysis of periodic patterns allows us to determine the dynamic time warping and affine scaling that aligns two gait sequences from similar viewpoints. We also show how gait alignment can be used to perform human identification and model-based body part segmentation.

Original languageEnglish (US)
Title of host publicationComputer Vision - 7th European Conference on Computer Vision, ECCV 2002, Proceedings
PublisherSpringer Verlag
Pages657-671
Number of pages15
Volume2351
ISBN (Print)9783540437444
StatePublished - 2002
Event7th European Conference on Computer Vision, ECCV 2002 - Copenhagen, Denmark
Duration: May 28 2002May 31 2002

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2351
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other7th European Conference on Computer Vision, ECCV 2002
CountryDenmark
CityCopenhagen
Period5/28/025/31/02

Fingerprint

Gait Analysis
Sequence Analysis
Gait
Tile
Identification (control systems)
Affine Scaling
Dynamic Time Warping
Avatar
Spatio-temporal Patterns
Silhouette
Symmetry Group
Alignment
Segmentation
Model-based
Angle
Estimate

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Liu, Y., Collins, R., & Tsin, Y. (2002). Gait sequence analysis using frieze patterns. In Computer Vision - 7th European Conference on Computer Vision, ECCV 2002, Proceedings (Vol. 2351, pp. 657-671). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2351). Springer Verlag.
Liu, Yanxi ; Collins, Robert ; Tsin, Yanghai. / Gait sequence analysis using frieze patterns. Computer Vision - 7th European Conference on Computer Vision, ECCV 2002, Proceedings. Vol. 2351 Springer Verlag, 2002. pp. 657-671 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Liu, Y, Collins, R & Tsin, Y 2002, Gait sequence analysis using frieze patterns. in Computer Vision - 7th European Conference on Computer Vision, ECCV 2002, Proceedings. vol. 2351, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2351, Springer Verlag, pp. 657-671, 7th European Conference on Computer Vision, ECCV 2002, Copenhagen, Denmark, 5/28/02.

Gait sequence analysis using frieze patterns. / Liu, Yanxi; Collins, Robert; Tsin, Yanghai.

Computer Vision - 7th European Conference on Computer Vision, ECCV 2002, Proceedings. Vol. 2351 Springer Verlag, 2002. p. 657-671 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2351).

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

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Liu Y, Collins R, Tsin Y. Gait sequence analysis using frieze patterns. In Computer Vision - 7th European Conference on Computer Vision, ECCV 2002, Proceedings. Vol. 2351. Springer Verlag. 2002. p. 657-671. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).