Occluded human action analysis using dynamic manifold model

This paper proposes a novel nonlinear manifold learning method for addressing the ill-posed problem of occluded human action analysis. As we know, a person can perform a broad variety of movements. To capture the multiplicity of a human action, this paper creates a low-dimensional manifold for capturing the intra-path and inter-path contexts of an event. Then, an action path matching scheme can be applied for seeking the best event path for linking the missed information between occluded persons. After that, a recovering scheme is proposed for repairing an occluded object to a complete one. Then, each action can be converted to a series of action primitives through posture analysis. Since occluded objects are handled, there will be many posture-symbol-converting errors. Instead of using a specific symbol, we code a posture using not only its best matched key posture but also its similarities among other key postures. Then, recognition of an action taken from occlude objects can be modeled as a matrix matching problem. With the matrix representation, different actions can be more robustly and effectively matched by comparing their Kullback-Leibler(KL) distances.