@inproceedings{ananthanarayanan2019demo,
author = {Ananthanarayanan, Ganesh and Bahl, Victor and Cox, Landon and Crown, Alex and Noghabi, Shadi and Shu, Yuanchao},
title = {Demo: Video Analytics - Killer App for Edge Computing},
booktitle = {ACM MobiSys},
year = {2019},
month = {June},
abstract = {The world is witnessing an unprecedented increase in camera deployment. The USA and UK, for instance, have one camera for every 8 people. Video analytics from these cameras are becoming more and more pervasive, exerting important functions on a wide range of verticals including manufacturing, transportation, and retails. While vision techniques have seen considerable advancement, they have come at the expense of compute and network cost.

As an alternative to the centralized, in-the-cloud compute paradigm, edge computing offers the promise of near real-time insights, faster localized actions, and cost reduction because of efficient data management and operations. We believe video analytics may represent the “killer application” for edge computing due to its demanding requirements on compute, bandwidth and latency. In this demo, we showcase a live video analytics system that spans across the cloud and edge, with low cost, and produces results with high accuracy.},
url = {http://approjects.co.za/?big=en-us/research/publication/demo-video-analytics-killer-app-for-edge-computing/},
}