# Video-based automatic lameness detection of dairy cows using pose estimation and multiple locomotion traits

arXiv: https://arxiv.org/abs/2401.05202 · HTML: https://mlsanity.com/paper/2401.05202/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 44 references that are in the map.
- First posted on arXiv 10 Jan 2024.

## Builds on (most cited first, 5 of 5)

- [Markerless tracking of user-defined features with deep learning](https://mlsanity.com/paper/1804.03142/) (2018)
- [Nested cross-validation when selecting classifiers is overzealous for most practical applications](https://mlsanity.com/paper/1809.09446/) (2018)
- [T-LEAP: Occlusion-robust pose estimation of walking cows using temporal information](https://mlsanity.com/paper/2104.08029/) (2021)
- [Comparison of 14 different families of classification algorithms on 115 binary datasets](https://mlsanity.com/paper/1606.00930/) (2016)
- [Segmentation Enhanced Lameness Detection in Dairy Cows from RGB and Depth Video](https://mlsanity.com/paper/2206.04449/) (2022)

## Built on by (most cited first, 5 of 5)

- [Consistent multi-animal pose estimation in cattle using dynamic Kalman filter based tracking](https://mlsanity.com/paper/2503.10450/) (2025)
- [Direct Video-Based Spatiotemporal Deep Learning for Cattle Lameness Detection](https://mlsanity.com/paper/2504.16404/) (2025)
- [AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock](https://mlsanity.com/paper/2507.22101/) (2025)
- [Lameness detection in dairy cows using pose estimation and bidirectional LSTMs](https://mlsanity.com/paper/2508.10643/) (2025)
- [FSMC-Pose: Frequency and Spatial Fusion with Multiscale Self-calibration for Cattle Mounting Pose Estimation](https://mlsanity.com/paper/2603.16596/) (2026)

## Abstract (arXiv)

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.