  Uncertainty estimation in deep neural networks is essential for designing
reliable and robust AI systems. Applications such as video surveillance for
identifying suspicious activities are designed with deep neural networks
(DNNs), but DNNs do not provide uncertainty estimates. Capturing reliable
uncertainty estimates in safety and security critical applications will help to
establish trust in the AI system. Our contribution is to apply Bayesian deep
learning framework to visual activity recognition application and quantify
model uncertainty along with principled confidence. We utilize the stochastic
variational inference technique while training the Bayesian DNNs to infer the
approximate posterior distribution around model parameters and perform Monte
Carlo sampling on the posterior of model parameters to obtain the predictive
distribution. We show that the Bayesian inference applied to DNNs provide
reliable confidence measures for visual activity recognition task as compared
to conventional DNNs. We also show that our method improves the visual activity
recognition precision-recall AUC by 6.2% compared to non-Bayesian baseline. We
evaluate our models on Moments-In-Time (MiT) activity recognition dataset by
selecting a subset of in- and out-of-distribution video samples.
