Combination of Annealing Particle Filter and Belief Propagation for 3D Upper Body Tracking
ICIRA '09 Proceedings of the 2nd International Conference on Intelligent Robotics and Applications
Simultaneous partitioned sampling for articulated object tracking
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Combination of annealing particle filter and belief propagation for 3D upper body tracking
Applied Bionics and Biomechanics - Personal Care Robotics
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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.