Activity detection of untrimmed CCTV ATM footage using 3D convolutional neural network

Aldi Hilman Ramadhani, Dina Chahyati

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

Abstract

This paper presents an approach to temporal human activity detection using the proposal then classification framework, which is one of the frameworks for temporal activity detection. The goal of this research is to detect and recognize certain activities at the ATM. We propose an activity detection method using a 3D convolutional neural network (3D CNN). Our proposed method achieved performance with the accuracy score of 93.94%, a precision of 96.36%, a recall of 93.94%, and an f-score of 93.69%.

Original languageEnglish
Title of host publication2020 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages357-362
Number of pages6
ISBN (Electronic)9781728192796
DOIs
Publication statusPublished - 17 Oct 2020
Event12th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020 - Virtual, Depok, Indonesia
Duration: 17 Oct 202018 Oct 2020

Publication series

Name2020 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020

Conference

Conference12th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020
CountryIndonesia
CityVirtual, Depok
Period17/10/2018/10/20

Keywords

  • Activity at the ATM
  • Activity detection
  • Activity recognition
  • Computer vision
  • Convolutional neural network

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