Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/29537
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Type: Conference paper
Title: 2D articulated tracking with dynamic Bayesian networks
Author: Shen, C.
Van Den Hengel, A.
Dick, A.
Brooks, M.
Citation: The Fourth International Conference on Computer and Information Technology : proceedings : September 14-16, 2004, Wuhan, China / Daming Wei (ed.), pp. 130-136
Publisher: IEEE
Publisher Place: Los Alamitos, California, USA
Issue Date: 2004
ISBN: 0769522165
Conference Name: International Conference on Computer and Information Technology (4th : 2004 : Wuhan, China)
Statement of
Responsibility: 
Chunhua Shen, Anton van den Hengel, Anthony Dick, Michael J. Brooks
Abstract: We present a novel method for tracking the motion of an articulated structure in a video sequence. The analysis of articulated motion is challenging because of the potentially large number of degrees of freedom (DOFs) of an articulated body. For particle filter based algorithms, the number of samples required with high dimensional problems can be computationally prohibitive. To alleviate this problem, we represent the articulated object as an undirected graphical model (or Markov Random Field, MRF) in which soft constraints between adjacent subparts are captured by conditional probability distributions. The graphical model is extended across time frames to implement a tracker. The tracking algorithm can be interpreted as a belief inference procedure on a dynamic Bayesian network. The discretisation of the state vectors makes it possible to utilise the efficient belief propagation (BP) and mean field (MF) algorithms to reason in this network. Experiments on real video sequences demonstrate that the proposed method is computationally efficient and performs well in tracking the human body.
Description: ©2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
RMID: 0020041883
DOI: 10.1109/CIT.2004.1357185
Appears in Collections:Computer Science publications

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