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RTSDE: recursive total-sum-distances-based density estimation approach and its application for autonomous real-time video analytics

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Published
Publication date9/12/2014
Host publication2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS)
Place of PublicationOrlando, FL, USA
PublisherIEEE
Pages81-86
Number of pages6
ISBN (Print)9781479944958
<mark>Original language</mark>English
Event2014 IEEE Symposium Series on Computational Intelligence - Florida, Orlando, United States
Duration: 9/12/201412/12/2014

Symposium

Symposium2014 IEEE Symposium Series on Computational Intelligence
Country/TerritoryUnited States
CityOrlando
Period9/12/1412/12/14

Symposium

Symposium2014 IEEE Symposium Series on Computational Intelligence
Country/TerritoryUnited States
CityOrlando
Period9/12/1412/12/14

Abstract

In this paper, we propose a new approach to data density estimation based on the total sum of distances from a data point, and the recently introduced Recursive Density Estimation technique. It is suitable for autonomous real-time video analytics problems, and has been specifically designed to be executed very fast; it uses integer-only arithmetic with no divisions and no floating point numbers (no FLOPs), making it particularly useful in situations where a hardware floating point unit may not be available, such as on embedded hardware and digital signal
processors, allowing for high definition video to be processed for novelty detection in real-time.

Bibliographic note

Date of Acceptance 06/09/2014