  Behavioral scoring of research data is crucial for extracting domain-specific
metrics but is bottlenecked on the ability to analyze enormous volumes of
information using human labor. Deep learning is widely viewed as a key
advancement to relieve this bottleneck. We identify one such domain, where deep
learning can be leveraged to alleviate the process of manual scoring. Novelty
preference paradigms have been widely used to study recognition memory in pigs,
but analysis of these videos requires human intervention. We introduce a subset
of such videos in the form of the 'Pig Novelty Preference Behavior' (PNPB)
dataset that is fully annotated with pig actions and keypoints. In order to
demonstrate the application of state-of-the-art action recognition models on
this dataset, we compare LRCN, C3D, and TSM on the basis of various analytical
metrics and discuss common pitfalls of the models. Our methods achieve an
accuracy of 93% and a mean Average Precision of 96% in estimating piglet
behavior.
  We open-source our code and annotated dataset at
https://github.com/AIFARMS/NOR-behavior-recognition
