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Open Access

Robust Recognition and Segmentation of Human Actions Using HMMs with Missing Observations

  • Patrick Peursum1Email author,
  • Hung H. Bui2,
  • Svetha Venkatesh1 and
  • Geoff West1
EURASIP Journal on Advances in Signal Processing20052005:870416

Received: 30 December 2003

Published: 15 August 2005


This paper describes the integration of missing observation data with hidden Markov models to create a framework that is able to segment and classify individual actions from a stream of human motion using an incomplete 3D human pose estimation. Based on this framework, a model is trained to automatically segment and classify an activity sequence into its constituent subactions during inferencing. This is achieved by introducing action labels into the observation vector and setting these labels as missing data during inferencing, thus forcing the system to infer the probability of each action label. Additionally, missing data provides recognition-level support for occlusions and imperfect silhouette segmentation, permitting the use of a fast (real-time) pose estimation that delegates the burden of handling undetected limbs onto the action recognition system. Findings show that the use of missing data to segment activities is an accurate and elegant approach. Furthermore, action recognition can be accurate even when almost half of the pose feature data is missing due to occlusions, since not all of the pose data is important all of the time.

Keywords and phrases

human motion analysisaction segmentationHMMs

Authors’ Affiliations

Department of Computing, Curtin University of Technology, Perth, Australia
Artificial Intelligence Center, SRI International, Menlo Park, USA


© Peursum et al. 2005