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Research article - Peer-reviewed, 2023

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

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


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.


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

Published in

International Journal of Computer Vision
2023, volume: 131, pages: 572-590
Publisher: SPRINGER

Authors' information

Broome, Sofia
Royal Institute of Technology
Feighelstein, Marcelo
University of Haifa
Zamansky, Anna
University of Haifa
Lencioni, Gabriel Carreira
Universidade de Sao Paulo
Andersen, Pia Haubro (Haubro Andersen, Pia)
Swedish University of Agricultural Sciences, Department of Clinical Sciences
Pessanha, Francisca
Utrecht University
Mahmoud, Marwa
University of Glasgow
Kjellstrom, Hedvig
Royal Institute of Technology
Salah, Albert Ali
Utrecht University
Salah, Albert Ali
Bogazici University

UKÄ Subject classification

Computer Vision and Robotics (Autonomous Systems)
Animal and Dairy Science

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