  Animal pose estimation has recently come into the limelight due to its
application in biology, zoology, and aquaculture. Deep learning methods have
effectively been applied to human pose estimation. However, the major
bottleneck to the application of these methods to animal pose estimation is the
unavailability of sufficient quantities of labeled data. Though there are ample
quantities of unlabelled data publicly available, it is economically
impractical to label large quantities of data for each animal. In addition, due
to the wide variety of body shapes in the animal kingdom, the transfer of
knowledge across domains is ineffective. Given the fact that the human brain is
able to recognize animal pose without requiring large amounts of labeled data,
it is only reasonable that we exploit unsupervised learning to tackle the
problem of animal pose recognition from the available, unlabelled data. In this
paper, we introduce a novel architecture that is able to recognize the pose of
multiple animals fromunlabelled data. We do this by (1) removing background
information from each image and employing an edge detection algorithm on the
body of the animal, (2) Tracking motion of the edge pixels and performing
agglomerative clustering to segment body parts, (3) employing contrastive
learning to discourage grouping of distant body parts together. Hence we are
able to distinguish between body parts of the animal, based on their visual
behavior, instead of the underlying anatomy. Thus, we are able to achieve a
more effective classification of the data than their human-labeled
counterparts. We test our model on the TigDog and WLD (WildLife Documentary)
datasets, where we outperform state-of-the-art approaches by a significant
margin. We also study the performance of our model on other public data to
demonstrate the generalization ability of our model.
