Going Deeper than Tracking: A Survey of Computer-Vision Based Recognition of Animal Pain and Emotions

Sofia Broomé, Marcelo Feighelstein, Anna Zamansky, Gabriel Carreira Lencioni, Pia Haubro Andersen, Francisca Pessanha, Marwa Mahmoud, Hedvig Kjellström, Albert Ali Salah

Research output: Contribution to journalArticlepeer-review

Abstract

Advances in animal motion tracking and pose recognition have been a game changer in the study of animal behavior. Recently, an increasing number of works go ‘deeper’ than tracking, and address automated recognition of animals’ internal states such as emotions and pain with the aim of improving animal welfare, making this a timely moment for a systematization of the field. This paper provides a comprehensive survey of computer vision-based research on recognition of pain and emotional states in animals, addressing both facial and bodily behavior analysis. We summarize the efforts that have been presented so far within this topic—classifying them across different dimensions, highlight challenges and research gaps, and provide best practice recommendations for advancing the field, and some future directions for research.

Original languageEnglish
JournalInternational Journal of Computer Vision
DOIs
StateAccepted/In press - 2022

Bibliographical note

Funding Information:
The research was partially supported by the grant from the Ministry of Science and Technology of Israel according to the research Project No. 19-57-06007. The second author was additionally supported by the Data Science Research Center (DSRC), University of Haifa. The authors would like to thank Ilan Shimshoni and Shir Amir for their scientific consultations.

Publisher Copyright:
© 2022, The Author(s).

Keywords

  • Affective computing
  • Computer vision for animals
  • Emotion recognition
  • Non-human behavior analysis
  • Pain estimation
  • Pain recognition

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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