Image fusion and influence function for performance improvement of ATM vandalism action recognition

Jeongseop Yun, Junyeop Lee, Seongkyu Mun, Chul Jin Cho, David K. Han, Hanseok Ko

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

Abstract

Rising rate of vandalism against Automatic Teller Machines (ATMs) is a serious issue within banking industries, prompting needs of a technology to autonomously recognize such events. A vision based fusion method proposed here for classifying these incidents is rooted on visually recognizing heavy or sharp objects potentially used for detecting vandalism actions inferred from optical flow. The recognition performance has been improved chiefly by a novel employment of influence functions in selecting data points of each class useful in learning. We show that the tool recognition performance can be improved when the training data is selected from the ImageNet data set as guided by the influence function.

Original languageEnglish
Title of host publicationProceedings of AVSS 2018 - 2018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538692943
DOIs
Publication statusPublished - 2019 Feb 11
Event15th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2018 - Auckland, New Zealand
Duration: 2018 Nov 272018 Nov 30

Publication series

NameProceedings of AVSS 2018 - 2018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance

Conference

Conference15th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2018
CountryNew Zealand
CityAuckland
Period18/11/2718/11/30

ASJC Scopus subject areas

  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Media Technology

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