  As herd size on dairy farms continues to increase, automatic health
monitoring of cows is gaining 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<EMAIL_ADDRESS>of 99%. On occluded data, our temporal approach
outperformed the static one by up to 32.9%, suggesting that using temporal data
was 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<EMAIL_ADDRESS>was of 93.8% on
known cows and 87.6% on unknown cows, indicating that the model was 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.
