Filtering perturbed in-vehicle pointing gesture trajectories: Improving the reliability of intent inference

Making a selection on an in-vehicle touchscreen entails undertaking a pointing gesture that can be subjected to a high level of perturbation due to road and/or driving conditions. This can lead to erroneous user input and requires further attention that would otherwise be available for driving. In this paper, we propose a low-complexity sequential Monte Carlo filtering method that removes the perturbations present in a highly non-linear pointing hand/finger trajectory. This latter is tracked using a 3D vision sensory device. The preprocessing introduced allows the intended destination on the interactive display to be determined, which can substantially reduce the duration of the pointing task and associated attention. The benefits of the proposed approach are illustrated using data from in-vehicle tests.