Quality of Information in Gathering Information via Video Analytics for Military Networks

Fidan Mehmeti, Noor Felemban, Zongqing Lu, Kristina Wheatman, Gregory Cirincione, Thomas F. La Porta

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Military networks, and especially tactical networks, have become crucial in conducting military actions. With images and videos being rich sources of information, a primary challenge in military networks is to be able to issue a query over a (large) distributed set of devices to find concrete actions or objects of interest. The problem inherent to this scenario is how to gather this information in an optimal way, depending on the objective, for further processing. To this end, in this work we present three different approaches, with different objectives, for using video analytics to label and collect the right information that is stored on mobile devices. We first present a solution for crowdsourcing devices to label videos that contain objects and actions of interest in a minimum amount of time. We then present a solution for gathering all images that contain objects of interest at a central server where a complete search of images stored on the mobile devices is completed as fast as possible. Finally, we consider the case in which we desire to collect images that contain objects of interest as fast as possible, perhaps at the expense of the overall search time taking longer. In all three cases, we divide processing responsibilities between mobile devices where the videos/images are stored and the edge cloud.

Original languageEnglish (US)
Article number9374636
Pages (from-to)50-55
Number of pages6
JournalIEEE Communications Magazine
Issue number2
StatePublished - Feb 2021

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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