Computer Vision for Primate Behavior Analysis in the Wild
Richard Vogg Timo Lüddecke Jonathan Henrich Sharmita Dey Matthias Nuske Valentin Hassler Derek Murphy Julia Fischer Julia Ostner Oliver Schülke Peter M. Kappeler Claudia Fichtel Alexander Gail Stefan Treue Hansjörg Scherberger Florentin Wörgötter Alexander S. Ecker
Advances in computer vision as well as increasingly widespread video-based
behavioral monitoring have great potential for transforming how we study animal
cognition and behavior. However, there is still a fairly large gap between the
exciting prospects and what can actually be achieved in practice today,
especially in videos from the wild. With this perspective paper, we want to
contribute towards closing this gap, by guiding behavioral scientists in what
can be expected from current methods and steering computer vision researchers
towards problems that are relevant to advance research in animal behavior. We
start with a survey of the state-of-the-art methods for computer vision
problems that are directly relevant to the video-based study of animal
behavior, including object detection, multi-individual tracking, (inter)action
recognition and individual identification. We then review methods for
effort-efficient learning, which is one of the biggest challenges from a
practical perspective. Finally, we close with an outlook into the future of the
emerging field of computer vision for animal behavior, where we argue that the
field should move fast beyond the common frame-by-frame processing and treat
video as a first-class citizen.