Human action recognition using spatio-temporal classification

Chin Hsien Fang, Ju Chin Chen, Chien Chung Tseng, Jenn Jier James Lien

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

8 Citations (Scopus)


In this paper a framework "Temporal-Vector Trajectory Learning" (TVTL) for human action recognition is proposed. In this framework, the major concept is that we would like to add the temporal information into the action recognition process. Base on this purpose, there are three kinds of temporal information, LTM, DTM, and TTM, being proposed. With the three kinds of proposed temporal information, the k-NN classifier based on the Mahanalobis distance metric do have better results than just using spatial information. The experimental results demonstrate that the method can recognize the actions well. Especially with our TTM and DTM framework, they do have great accuracy rates. Even with noisy data, the framework still have good performance.

Original languageEnglish
Title of host publicationComputer Vision, ACCV 2009 - 9th Asian Conference on Computer Vision, Revised Selected Papers
Number of pages12
EditionPART 2
Publication statusPublished - 2010
Event9th Asian Conference on Computer Vision, ACCV 2009 - Xi'an, China
Duration: 2009 Sep 232009 Sep 27

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume5995 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other9th Asian Conference on Computer Vision, ACCV 2009

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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