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

In recent years, we have witnessed an explosive growth of data. Much of thisdata is video data generated by security cameras, smartphones, and dash cams.The timely analysis of such data is of great practical importance for manyemerging applications, such as real-time facial recognition and objectdetection. In this study, we address the problem of real-time in-situ videoanalytics with dash cam videos and present EdgeDashAnalytics (EDA), anedge-based system that enables near real-time video analytics using a localnetwork of mobile devices. In particular, it simultaneously processes videosproduced by two dash cams of different angles with one or more mobile deviceson the move in a near real-time manner. One camera faces outward to capture theview in front of the vehicle, while the other camera faces inward to capturethe driver. The outer videos are analysed to detect potential driving hazards,while the inner videos are used to identify driver distractedness. EDA achievesnear real-time video analytics using resource-constrained, transient mobiledevices by devising and incorporating several optimisations, with a tolerableloss in accuracy. We have implemented EDA as an Android app and evaluated itusing two dash cams and several heterogeneous mobile devices with the BDD100Kdash cam video dataset (arXiv:1805.04687 [cs.CV]) and the DMD driver monitoringdataset (arXiv:2008.12085 [cs.CV]). Experiment results demonstrate thefeasibility of real-time video analytics in terms of turnaround time and energyconsumption (or battery usage), using resource-constrained mobile devices onthe move.

Distributed Edge-based Video Analytics on the Move