Application for semantic segmentation models in areas such as autonomous
vehicles and human computer interaction require real-time predictive
capabilities. The challenges of addressing real-time application is amplified
by the need to operate on resource constrained hardware. Whilst development of
real-time methods for these platforms has increased, these models are unable to
sufficiently reason about uncertainty present. This paper addresses this by
combining deep feature extraction from pre-trained models with Bayesian
regression and moment propagation for uncertainty aware predictions. We
demonstrate how the proposed method can yield meaningful uncertainty on
embedded hardware in real-time whilst maintaining predictive performance.Comment: 6 pages, 3 figure
