no code implementations • 8 Jun 2023 • Ernest Pokropek, Sofia Broomé, Pia Haubro Andersen, Hedvig Kjellström
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.
1 code implementation • 22 Dec 2021 • Sofia Broomé, Ernest Pokropek, Boyu Li, Hedvig Kjellström
Most action recognition models today are highly parameterized, and evaluated on datasets with appearance-wise distinct classes.
1 code implementation • 30 Aug 2021 • Maheen Rashid, Sofia Broomé, Katrina Ask, Elin Hernlund, Pia Haubro Andersen, Hedvig Kjellström, Yong Jae Lee
Consequently, a pragmatic equine pain classification system would use video of the unobserved horse and weak labels.
no code implementations • 18 Jun 2021 • Ci Li, Nima Ghorbani, Sofia Broomé, Maheen Rashid, Michael J. Black, Elin Hernlund, Hedvig Kjellström, Silvia Zuffi
In this paper we present our preliminary work on model-based behavioral analysis of horse motion.
1 code implementation • 21 May 2021 • Sofia Broomé, Katrina Ask, Maheen Rashid, Pia Haubro Andersen, Hedvig Kjellström
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.
1 code implementation • 17 Feb 2021 • Zhenghong Li, Sofia Broomé, Pia Haubro Andersen, Hedvig Kjellström
To automate parts of this process, we propose a Deep Learning-based method to detect EquiFACS units automatically from images.
2 code implementations • 2 Feb 2020 • Joonatan Mänttäri, Sofia Broomé, John Folkesson, Hedvig Kjellström
A number of techniques for interpretability have been presented for deep learning in computer vision, typically with the goal of understanding what the networks have based their classification on.
1 code implementation • 7 Jan 2019 • Sofia Broomé, Karina Bech Gleerup, Pia Haubro Andersen, Hedvig Kjellström
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.