Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  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,  represent the articulated object as an undirected graphical model (or Markov random field, MRF) in which soft constraints beten 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 ll in tracking the human body.
['Chunhua Shen', 'A. van den Hengel', 'Anthony R. Dick', 'Michael J. Brooks']
I am writing a research paper on a idea 2D articulated tracking with dynamic Bayesian networks
[{"from": "human", "value": "I am writing a research paper on a idea 2D articulated tracking with dynamic Bayesian networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  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,  represent the articulated object as an undirected graphical model (or Markov random field, MRF) in which soft constraints beten 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 ll in tracking the human body."}]