Fusion of human posture features for continuous action recognition

Khai Tran, Ioannis A. Kakadiaris, Shishir K. Shah

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


This paper presents a real-time or online system for continuous recognition of human actions. The system recognizes actions such as walking, bending, jumping, waving, and falling and relies on spatial features computed to characterize human posture. The paper evaluates the utility of these features based on its joint or independent treatment within the context of the Hidden Markov Model (HMM) framework. A baseline approach wherein disparate spatial features are treated as an input vector to trained HMMs is used to compare three different independent feature models. In addition, an action transition constraints is introduced to stabilize the developed models and allow for continuity in recognized actions. The system is evaluated across a dataset of videos and results reported in terms of frame error rate, the frame delay in recognizing an action, action recognition rate, and the missed and false recognition rates. Experimental results shows the effectiveness of the proposed treatment of input features and the corresponding HMM formulations.

Original languageEnglish (US)
Pages (from-to)244-257
Number of pages14
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6553 LNCS
Issue numberPART 1
StatePublished - 2012
Event11th European Conference on Computer Vision, ECCV 2010 - Heraklion, Crete, Greece
Duration: Sep 10 2010Sep 11 2010


  • Contiuous Action Recognition
  • Fusion of Features
  • HMMs

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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