Dynamic Bayesian Network for Time-Dependent Classification Problems in Robotics

Abstract

This chapter discusses the use of dynamic Bayesian networks (DBNs) for time-dependent classification problems in mobile robotics, where Bayesian inference is used to infer the class, or category of interest, given the observed data and prior knowledge. Formulating the DBN as a time-dependent classification problem, and by making some assumptions, a general expression for a DBN is given in terms of classifier priors and likelihoods through the time steps. Since multi-class problems are addressed, and because of the number of time slices in the model, additive smoothing is used to prevent the values of priors from being close to zero. To demonstrate the effectiveness of DBN in time-dependent classification problems, some experimental results are reported regarding semantic place recognition and daily-activity classification.

Understanding of Human Behavior with a Robotic Agent Through Daily Activity Analysis

“…In this work the authors defined a new set of descriptors, suitable to operate in realistic data, while developed a computational model to segment temporal intervals with social interaction or individual behavior and tested the method on their own publicly available dataset. In addition, the authors in [31] demonstrated the effectiveness of DBN in time-dependent classification problems, where in their work reported experimental results regarding semantic place recognition and daily-activity classification.…”