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
@article{arxiv.2106.12181,
title = {Vision-based Behavioral Recognition of Novelty Preference in Pigs},
author = {Aniket Shirke and Rebecca Golden and Mrinal Gautam and Angela Green-Miller and Matthew Caesar and Ryan N. Dilger},
journal= {arXiv preprint arXiv:2106.12181},
year = {2021}
}
Comments
5 pages, 7 figures, Accepted at the CVPR 2021 CV4Animals workshop