Animal behavior classification using DeepLabCut

Shiori Fujimori, Takaaki Ishikawa, Hiroshi Watanabe

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

Abstract

In this paper, we introduce a method to classify animal behaviors from videos taken by a fixed-point camera. In order to classify animal behavior, it is necessary to detect and track the animals. Conventional approaches for detecting moving objects are based on background subtraction and frame subtraction. Conventional methods are not suitable for detection of animals kept indoors since they are susceptible to sunlight and shadow. We propose a method to track animals and classify their behavior using skeletal information obtained by DeepLabCut. The experimental results show that the proposed method is superior to the conventional method.

Original languageEnglish
Title of host publication2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages254-257
Number of pages4
ISBN (Electronic)9781728198026
DOIs
Publication statusPublished - 2020 Oct 13
Event9th IEEE Global Conference on Consumer Electronics, GCCE 2020 - Kobe, Japan
Duration: 2020 Oct 132020 Oct 16

Publication series

Name2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020

Conference

Conference9th IEEE Global Conference on Consumer Electronics, GCCE 2020
Country/TerritoryJapan
CityKobe
Period20/10/1320/10/16

Keywords

  • DeepLabCut
  • animal behavior
  • deep learning

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Media Technology
  • Instrumentation
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition

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