Modeling and predicting human and vehicle motion is an active research domain.
Owing to the difficulty in modeling the various factors that determine motion
(e.g. internal state, perception) this is often tackled by applying machine
learning techniques to build a statistical model, using as input a collection
of trajectories gathered through a sensor (e.g. camera, laser scanner), and then
using that model to predict further motion. Unfortunately, most current
techniques use offline learning algorithms, meaning that they are not able to
learn new motion patterns once the learning stage has finished.
This books presents a lifelong learning approach where motion patterns can be
learned incrementally, and in parallel with prediction. The approach is based on
a novel extension to hidden Markov models, and the main contribution presented
in this book, called growing hidden Markov models, which gives us the ability to
learn incrementally both the parameters and the structure of the model. The
proposed approach has been extensively validated with synthetic and real
trajectory data. In our experiments our approach consistently learned motion
models that were more compact and accurate than those produced by two other
state-of-the-art techniques, confirming the viability of lifelong learning
approaches to build human behavior models.