English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Smart cameras are increasingly used in surveillance solutions in publicspaces. Contemporary computer vision applications can be used to recognizeevents that require intervention by emergency services. Smart cameras can bemounted in locations where citizens feel particularly unsafe, e.g., pathwaysand underpasses with a history of incidents. One promising approach for smartcameras is edge AI, i.e., deploying AI technology on IoT devices. However,implementing resource-demanding technology such as image recognition using deepneural networks (DNN) on constrained devices is a substantial challenge. Inthis paper, we explore two approaches to reduce the need for compute incontemporary image recognition in an underpass. First, we showcase successfulneural network pruning, i.e., we retain comparable classification accuracy withonly 1.1\% of the neurons remaining from the state-of-the-art DNN architecture.Second, we demonstrate how a CycleGAN can be used to transformout-of-distribution images to the operational design domain. We posit that bothpruning and CycleGANs are promising enablers for efficient edge AI in smartcameras.

Enabling Image Recognition on Constrained Devices Using Neural Network Pruning and a CycleGAN