Multi-view gymnastic activity recognition with fused HMM

Ying Wang*, Kaiqi Huang, Tieniu Tan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

5 Citations (Scopus)


More and more researchers focus their studies on multi-view activity recognition, because a fixed view could not provide enough information for recognition. In this paper, we use multi-view features to recognize six kinds of gymnastic activities. Firstly, shape-based features are extracted from two orthogonal cameras in the form of R transform. Then a multi-view approach based on Fused HMM is proposed to combine different features for similar gymnastic activity recognition. Compared with other activity models, our method achieves better performance even in the case of frame loss.

Original languageEnglish
Title of host publicationComputer Vision - ACCV 2007 - 8th Asian Conference on Computer Vision, Proceedings
Place of PublicationTokyo, Japan
PublisherSpringer, Springer Nature
Number of pages11
Volume4843 LNCS
EditionPART 1
ISBN (Electronic)9783540763864
ISBN (Print)9783540763857
Publication statusPublished - 2007
Externally publishedYes
Event8th Asian Conference on Computer Vision, ACCV 2007 - Tokyo, Japan
Duration: 18 Nov 200722 Nov 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume4843 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349


Other8th Asian Conference on Computer Vision, ACCV 2007


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