Visual surveillance transformer

Keong Hun Choi, Jong Eun Ha

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

In a visual surveillance system, even the same object should exhibit different detection results depending on the surrounding environment configuration. To this end, the model for visual surveillance needs to detect an object by understanding the state of the object according to the environment on the image. In this study, for such visual surveillance, an object segmentation model applied with a transformer structure suitable for image processing was used to divide objects inside the image into foreground and background. A modified attention structure was presented for the corresponding transformer structure, and the results of object segmentation models according to the type of input data were compared.

Original languageEnglish
Pages (from-to)972-977
Number of pages6
JournalJournal of Institute of Control, Robotics and Systems
Volume27
Issue number12
DOIs
StatePublished - 2021

Keywords

  • Deep learning
  • Segmentation
  • Transformer
  • Visual surveillance

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