  As herd size on dairy farms continue to increase, automatic health monitoring
of cows has gained in interest. Lameness, a prevalent health disorder in dairy
cows, is commonly detected by analyzing the gait of cows. A cow's gait can be
tracked in videos using pose estimation models because models learn to
automatically localize anatomical landmarks in images and videos. Most animal
pose estimation models are static, that is, videos are processed frame by frame
and do not use any temporal information. In this work, a static deep-learning
model for animal-pose-estimation was extended to a temporal model that includes
information from past frames. We compared the performance of the static and
temporal pose estimation models. The data consisted of 1059 samples of 4
consecutive frames extracted from videos (30 fps) of 30 different dairy cows
walking through an outdoor passageway. As farm environments are prone to
occlusions, we tested the robustness of the static and temporal models by
adding artificial occlusions to the videos. The experiments showed that, on
non-occluded data, both static and temporal approaches achieved a Percentage of
Correct Keypoints (PCKh@0.2) of 99%. On occluded data, our temporal approach
outperformed the static one by up to 32.9%, suggesting that using temporal data
is beneficial for pose estimation in environments prone to occlusions, such as
dairy farms. The generalization capabilities of the temporal model was
evaluated by testing it on data containing unknown cows (cows not present in
the training set). The results showed that the average detection rate
(PCKh@0.2) was of 93.8% on known cows and 87.6% on unknown cows, indicating
that the model is capable of generalizing well to new cows and that they could
be easily fine-tuned to new herds. Finally, we showed that with harder tasks,
such as occlusions and unknown cows, a deeper architecture was more beneficial.
