Going Deeper than Tracking: a Survey of Computer-Vision Based Recognition of Animal Pain and Affective States
This work systematizes a growing field aimed at improving animal welfare through automated state recognition, but it is incremental as it primarily reviews and organizes existing literature.
This survey addresses the problem of automated recognition of animal pain and affective states using computer vision, summarizing existing research, highlighting challenges, and providing recommendations for advancing the field.
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 affective states and pain 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.