Search Results for author:
Found 9 papers, 6 papers with code
Predictive Modeling of Equine Activity Budgets Using a 3D Skeleton Reconstructed from Surveillance Recordings
In this work, we present a pipeline to reconstruct the 3D pose of a horse from 4 simultaneous surveillance camera recordings.
Going Deeper than Tracking: a Survey of Computer-Vision Based Recognition of Animal Pain and Affective States
Advances in animal motion tracking and pose recognition have been a game changer in the study of animal behavior.
Recur, Attend or Convolve? On Whether Temporal Modeling Matters for Cross-Domain Robustness in Action Recognition
Most action recognition models today are highly parameterized, and evaluated on datasets with appearance-wise distinct classes.
Equine Pain Behavior Classification via Self-Supervised Disentangled Pose Representation
Consequently, a pragmatic equine pain classification system would use video of the unobserved horse and weak labels.
hSMAL: Detailed Horse Shape and Pose Reconstruction for Motion Pattern Recognition
In this paper we present our preliminary work on model-based behavioral analysis of horse motion.
Sharing Pain: Using Pain Domain Transfer for Video Recognition of Low Grade Orthopedic Pain in Horses
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.
Automated Detection of Equine Facial Action Units
To automate parts of this process, we propose a Deep Learning-based method to detect EquiFACS units automatically from images.
Interpreting video features: a comparison of 3D convolutional networks and convolutional LSTM networks
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.
Dynamics are Important for the Recognition of Equine Pain in Video
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.
