2D articulated tracking with dynamic Bayesian networks
Date
2004
Authors
Shen, C.
Van Den Hengel, A.
Dick, A.
Brooks, M.
Editors
Wei, D.
Wang, H.
Peng, Z.
Kara, A.
He, Y.
Wang, H.
Peng, Z.
Kara, A.
He, Y.
Advisors
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Volume Title
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Conference paper
Citation
The Fourth International Conference on Computer and Information Technology : proceedings : September 14-16, 2004, Wuhan, China / Daming Wei (ed.), pp. 130-136
Statement of Responsibility
Chunhua Shen, Anton van den Hengel, Anthony Dick, Michael J. Brooks
Conference Name
International Conference on Computer and Information Technology (4th : 2004 : Wuhan, China)
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.
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