hSMAL: Detailed Horse Shape and Pose Reconstruction for Motion Pattern Recognition
This work addresses lameness detection in horses, which is an incremental improvement in veterinary diagnostics using 3D modeling.
The authors tackled the problem of detailed horse shape and pose reconstruction for motion pattern recognition by developing a novel hSMAL model based on 37 horse toys, and applied it to lameness detection from video, showing benefits over mocap-based methods.
In this paper we present our preliminary work on model-based behavioral analysis of horse motion. Our approach is based on the SMAL model, a 3D articulated statistical model of animal shape. We define a novel SMAL model for horses based on a new template, skeleton and shape space learned from $37$ horse toys. We test the accuracy of our hSMAL model in reconstructing a horse from 3D mocap data and images. We apply the hSMAL model to the problem of lameness detection from video, where we fit the model to images to recover 3D pose and train an ST-GCN network on pose data. A comparison with the same network trained on mocap points illustrates the benefit of our approach.