A Method for Online Analysis of Structured Processes Using Bayesian Filters and Echo State Networks

We propose a Bayesian filtering framework for online analysis of visual structured processes, which can be combined with the Echo State Network (ESN) to capture prior information. With the proposed method we mitigate the effective Markovian Behavior of the ESN. We are able to keep a set of hypotheses about the entire history of behaviors and to evaluate them online based on new observations. The performance is evaluated under two complex visual behavior understanding scenarios using public datasets: a visual process for a kitchen table preparation and a real life manufacturing process.