A Bayesian Framework for Intent Prediction in Object Tracking
Bashar I. Ahmad, Patrick Langdon, Simon Godsill · 2019
In this paper, we introduce a generic Bayesian framework for inferring the intent of a tracked object, as early as possible, based on the available partial sensory observations. It treats the prediction problem, i.e. not estimating the object state such as position, within an object tracking formulation. This leads to a low-complexity implementation of the inference routine with minimal training requirements. The proposed approach utilises suitable stochastic, namely linear Gaussian, models to capture long term dependencies in the object trajectory as dictated by intent. Numerical examples are shown to demonstrate the efficacy of this framework.