Video-based Automatic Lameness Detection of Dairy Cows using Pose
  Estimation and Multiple Locomotion Traits
Helena Russello Rik van der Tol Menno Holzhauer Eldert J. van Henten Gert Kootstra
This study presents an automated lameness detection system that uses
deep-learning image processing techniques to extract multiple locomotion traits
associated with lameness. Using the T-LEAP pose estimation model, the motion of
nine keypoints was extracted from videos of walking cows. The videos were
recorded outdoors, with varying illumination conditions, and T-LEAP extracted
99.6% of correct keypoints. The trajectories of the keypoints were then used to
compute six locomotion traits: back posture measurement, head bobbing, tracking
distance, stride length, stance duration, and swing duration. The three most
important traits were back posture measurement, head bobbing, and tracking
distance. For the ground truth, we showed that a thoughtful merging of the
scores of the observers could improve intra-observer reliability and agreement.
We showed that including multiple locomotion traits improves the classification
accuracy from 76.6% with only one trait to 79.9% with the three most important
traits and to 80.1% with all six locomotion traits.