DeepCounter: Using deep learning to count garbage bags

Kazuhiro Mikami, Yin Chen, Jin Nakazawa, Yasuhiro Iida, Yasunari Kishimoto, Yu Oya

研究成果: Conference contribution

11 被引用数 (Scopus)

抄録

This paper proposes DeepCounter, an automotive sensing system where deep learning based image processing technology 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 in order to sense a fine-grain spatio-temporal distribution on the amount of disposed garbage in cities that is envisioned to be helpful to develop novel applications related to garbage collection there. A prototype system is implemented on a GPU-integrated signal-board computer. A detection-tracking-counting (DTC) algorithm is developed and implemented based on the single shot multibox detector (SSD), a well-known real-time object detection algorithm. Experimental evaluation validates the feasibility of the proposed approach using video of realistic garbage collection in Fujisawa city, Japan.

本文言語English
ホスト出版物のタイトルProceedings - 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1-10
ページ数10
ISBN(電子版)9781538677599
DOI
出版ステータスPublished - 2019 1月 9
イベント24th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018 - Hakodate, Japan
継続期間: 2018 8月 292018 8月 31

出版物シリーズ

名前Proceedings - 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018

Conference

Conference24th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018
国/地域Japan
CityHakodate
Period18/8/2918/8/31

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ネットワークおよび通信
  • コンピュータ サイエンスの応用

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