Real-time Occupancy Correction Method for 3D Stereovision Counting Cameras

Fisayo Caleb Sangogboye, Mikkel Baun Kjærgaard

Research output: Chapter in Book/Report/Conference proceedingConference abstract in proceedingsResearchpeer-review

Abstract

In this poster, we present an occupancy count correction method - PreCount that corrects the count errors of camera sensing technologies in real-time. PreCount utilizes supervised machine learning approach to learn error paerns from previous corrections alongside some contextual factors that are responsible for the propagation of these errors. In our evaluation, we compare PreCount with state-of-art methods using the normalized root mean squared error metric (NRMSE) with datasets from four building cases. e obtained evaluation results using ground truth data indicates that PreCount can achieve an error reduction of 68% when compared to raw counts and state-of-art methods.

Original languageEnglish
Title of host publicationProceedings of the 16th ACM Conference on Embedded Networked Sensor Systems
PublisherAssociation for Computing Machinery
Publication date4. Nov 2018
Pages402-403
ISBN (Electronic)978-1-4503-5952-8
DOIs
Publication statusPublished - 4. Nov 2018
Event16th ACM Conference on Embedded Networked Sensor Systems - Shenzhen, China
Duration: 4. Nov 20187. Nov 2018

Conference

Conference16th ACM Conference on Embedded Networked Sensor Systems
Country/TerritoryChina
CityShenzhen
Period04/11/201807/11/2018

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