CNN-Based Boat Detection Model for Alert System Using Surveillance Video Camera

Tatsuhiro Akiyama, Yosuke Kobayashi, Jay Kishigami, Kenji Muto

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

3 Citations (Scopus)

Abstract

In Tokyo, various boats pass through the canal on the bayside. The loud sound created by these boats may cause some stress to the residents in that area. We propose a boat detection model based on convolutional neural networks (CNNs) using VGG19 that is trained using several types of boat pictures. Our proposed model aims to detect the type of boat passing through the canal using images obtained from the surveillance video camera. We finally achieve a practical result as F1-score of 0.70 by the proposed model.

Original languageEnglish
Title of host publication2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages758-759
Number of pages2
ISBN (Electronic)9781538663097
DOIs
Publication statusPublished - 2018 Dec 12
Event7th IEEE Global Conference on Consumer Electronics, GCCE 2018 - Nara, Japan
Duration: 2018 Oct 92018 Oct 12

Publication series

Name2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018

Other

Other7th IEEE Global Conference on Consumer Electronics, GCCE 2018
CountryJapan
CityNara
Period18/10/918/10/12

Keywords

  • Boat classification
  • Boat detection
  • Convolutional Neural Networks
  • Image Recognition
  • Surveillance Video Camera

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality
  • Instrumentation

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  • Cite this

    Akiyama, T., Kobayashi, Y., Kishigami, J., & Muto, K. (2018). CNN-Based Boat Detection Model for Alert System Using Surveillance Video Camera. In 2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018 (pp. 758-759). [8574704] (2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GCCE.2018.8574704