Poster abstract: Using deep learning to count garbage bags

Kazuhiro Mikami, Yin Chen, Jin Nakazawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

The information of daily garbage diposal can be used to develop many appealing applications in smart cities. This poster introduces DeepCounter, an automotive sensing system to providing a fine-grained spatio-temporal distribution on the amount of disposed garbage bags. In the system, deep learning based image processing is used to automatically count the number of collected garbage bags from the video taken by a camera mounted on the rear of a garbage truck. A prototype system is implemented and experimental evaluation validates the feasibility of our proposal using realistic garbage collection videos in Fujisawa city, Japan.

Original languageEnglish
Title of host publicationSenSys 2018 - Proceedings of the 16th Conference on Embedded Networked Sensor Systems
PublisherAssociation for Computing Machinery, Inc
Pages329-330
Number of pages2
ISBN (Electronic)9781450359528
DOIs
Publication statusPublished - 2018 Nov 4
Event16th ACM Conference on Embedded Networked Sensor Systems, SENSYS 2018 - Shenzhen, China
Duration: 2018 Nov 42018 Nov 7

Publication series

NameSenSys 2018 - Proceedings of the 16th Conference on Embedded Networked Sensor Systems

Conference

Conference16th ACM Conference on Embedded Networked Sensor Systems, SENSYS 2018
CountryChina
CityShenzhen
Period18/11/418/11/7

Keywords

  • Automotive sensing
  • Deep learning
  • Image processing
  • Smart cities
  • Urban sensing

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Computer Networks and Communications

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