By Topic

Human posture recognition in video sequence using pseudo 2-D hidden Markov models

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Lim Hock Wyi Aloysius ; Nat. Univ. of Singapore, Singapore ; Guo Dong ; Huang Zhiyong ; Tan, T.

This paper describes a video surveillance system capable of recognizing human postures from video sequences. The system comprises of two key modules: human detection and posture classification. In the module of human detection, human blobs are extracted by the technique of background subtraction. An adaptive background model is employed to characterize the dynamics and complexity of outdoor scenes based on the mixture of Gaussians. In order to formulate the variations of human postures, pseudo 2D hidden Markov models (P2DHMM) is employed for representing and recognizing human postures based on its '2-D elastic matching' property. It is trained to differentiate human postures and tolerate the variations of the same human posture using embedded Viterbi and segmental K means algorithms. In the classification of human postures, observation sequence is extracted from current image frame. The probabilities of observation sequence corresponding to each P2DHMM model are computed by doubly embedded Viterbi optimization, and human blob is classified as the human posture with the highest likelihood.

Published in:

Control, Automation, Robotics and Vision Conference, 2004. ICARCV 2004 8th  (Volume:1 )

Date of Conference:

6-9 Dec. 2004