In this work, we present a pipeline to reconstruct the 3D pose of a horse from 4 simultaneous surveillance camera recordings.
no code implementations • 16 Jun 2022 • Sofia Broomé, Marcelo Feighelstein, Anna Zamansky, Gabriel Carreira Lencioni, Pia Haubro Andersen, Francisca Pessanha, Marwa Mahmoud, Hedvig Kjellström, Albert Ali Salah
Advances in animal motion tracking and pose recognition have been a game changer in the study of animal behavior.
Consequently, a pragmatic equine pain classification system would use video of the unobserved horse and weak labels.
Moreover, we present a human expert baseline for the problem, as well as an extensive empirical study of various domain transfer methods and of what is detected by the pain recognition method trained on clean experimental pain in the orthopedic dataset.
To automate parts of this process, we propose a Deep Learning-based method to detect EquiFACS units automatically from images.
Sequential models are experimentally compared to single-frame models, showing the importance of the temporal dimension of the data, and are benchmarked against a veterinary expert classification of the data.