DeepCounter: Using deep learning to count garbage bags

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

doi:10.1109/RTCSA.2018.00010

DeepCounter: Using deep learning to count garbage bags

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

Faculty of Environment and Information Studies

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

11 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Proceedings - 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-10
Number of pages	10
ISBN (Electronic)	9781538677599
DOIs	https://doi.org/10.1109/RTCSA.2018.00010
Publication status	Published - 2019 Jan 9
Event	24th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018 - Hakodate, Japan Duration: 2018 Aug 29 → 2018 Aug 31

Publication series

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

Conference

Conference	24th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018
Country/Territory	Japan
City	Hakodate
Period	18/8/29 → 18/8/31

Keywords

Automotive sensing
Deep learning
Image processing
Smart cities
Urban sensing

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/RTCSA.2018.00010

Cite this

Mikami, K., Chen, Y., Nakazawa, J., Iida, Y., Kishimoto, Y., & Oya, Y. (2019). DeepCounter: Using deep learning to count garbage bags. In Proceedings - 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018 (pp. 1-10). Article 8607228 (Proceedings - 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RTCSA.2018.00010

DeepCounter: Using deep learning to count garbage bags. / Mikami, Kazuhiro; Chen, Yin; Nakazawa, Jin et al.
Proceedings - 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1-10 8607228 (Proceedings - 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Mikami, K, Chen, Y, Nakazawa, J, Iida, Y, Kishimoto, Y & Oya, Y 2019, DeepCounter: Using deep learning to count garbage bags. in Proceedings - 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018., 8607228, Proceedings - 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1-10, 24th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018, Hakodate, Japan, 18/8/29. https://doi.org/10.1109/RTCSA.2018.00010

Mikami K, Chen Y, Nakazawa J, Iida Y, Kishimoto Y, Oya Y. DeepCounter: Using deep learning to count garbage bags. In Proceedings - 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1-10. 8607228. (Proceedings - 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018). doi: 10.1109/RTCSA.2018.00010

Mikami, Kazuhiro ; Chen, Yin ; Nakazawa, Jin et al. / DeepCounter : Using deep learning to count garbage bags. Proceedings - 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1-10 (Proceedings - 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018).

@inproceedings{ede524b1e8aa41c798936c5ef20bdf41,

title = "DeepCounter: Using deep learning to count garbage bags",

abstract = "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.",

keywords = "Automotive sensing, Deep learning, Image processing, Smart cities, Urban sensing",

author = "Kazuhiro Mikami and Yin Chen and Jin Nakazawa and Yasuhiro Iida and Yasunari Kishimoto and Yu Oya",

note = "Funding Information: This work was partially supported by JSPS Grant-in-Aid for Young Scientists (B) Grant Number 17K12677. The help from Fujisawa City is greatly appreciated. Publisher Copyright: {\textcopyright} 2018 IEEE.; 24th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018 ; Conference date: 29-08-2018 Through 31-08-2018",

year = "2019",

month = jan,

day = "9",

doi = "10.1109/RTCSA.2018.00010",

language = "English",

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

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1--10",

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

}

TY - GEN

T1 - DeepCounter

T2 - 24th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018

AU - Mikami, Kazuhiro

AU - Chen, Yin

AU - Nakazawa, Jin

AU - Iida, Yasuhiro

AU - Kishimoto, Yasunari

AU - Oya, Yu

N1 - Funding Information: This work was partially supported by JSPS Grant-in-Aid for Young Scientists (B) Grant Number 17K12677. The help from Fujisawa City is greatly appreciated. Publisher Copyright: © 2018 IEEE.

PY - 2019/1/9

Y1 - 2019/1/9

N2 - 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.

AB - 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.

KW - Automotive sensing

KW - Deep learning

KW - Image processing

KW - Smart cities

KW - Urban sensing

UR - http://www.scopus.com/inward/record.url?scp=85061719711&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85061719711&partnerID=8YFLogxK

U2 - 10.1109/RTCSA.2018.00010

DO - 10.1109/RTCSA.2018.00010

M3 - Conference contribution

AN - SCOPUS:85061719711

T3 - Proceedings - 2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2018

SP - 1

EP - 10

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

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 29 August 2018 through 31 August 2018

ER -

DeepCounter: Using deep learning to count garbage bags

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this