Human posture reconstruction based on posture probability density

In this paper, we propose a human posture reconstruction method from the insufficient input posture data based on human posture probability density that is constructed by a long-term human motion capture data. Since the long continuous daily human motion data has high dimensions and becomes huge size, the human posture data should be effectively compressed. The long term posture data has nonlinear distribution on the posture space, since each specific posture such as standing and sitting has different property. The posture data is allocated into some subspaces and compressed for each subspace with mixtures of probabilistic principal component analyzer (MPPCA). MPPCA is improved by replacing conventional EM algorithm with deterministic annealing EM algorithm (DAEM) to avoid initial parameter sensitivity. The posture probability density is constructed over those subspaces. The adequate human posture can be reconstructed from the insufficient data by introducing the posture probability density into the sequential Monte Carlo framework. The experimental results show that the robust human posture estimation can be realized since this method does not estimate the unique posture but estimates the proper posterior posture density with using the posture prior knowledge.