  Smart cameras are increasingly used in surveillance solutions in public
spaces. Contemporary computer vision applications can be used to recognize
events that require intervention by emergency services. Smart cameras can be
mounted in locations where citizens feel particularly unsafe, e.g., pathways
and underpasses with a history of incidents. One promising approach for smart
cameras is edge AI, i.e., deploying AI technology on IoT devices. However,
implementing resource-demanding technology such as image recognition using deep
neural networks (DNN) on constrained devices is a substantial challenge. In
this paper, we explore two approaches to reduce the need for compute in
contemporary image recognition in an underpass. First, we showcase successful
neural network pruning, i.e., we retain comparable classification accuracy with
only 1.1\% of the neurons remaining from the state-of-the-art DNN architecture.
Second, we demonstrate how a CycleGAN can be used to transform
out-of-distribution images to the operational design domain. We posit that both
pruning and CycleGANs are promising enablers for efficient edge AI in smart
cameras.
