Unsupervised detection of mouse behavioural anomalies using two-stream convolutional autoencoders

Nwokedi, Ezechukwu I, Bains, Rasneer S, Bidaut, Luc , Wells, Sara, Ye, Xujiong and Brown, James M (2021) Unsupervised detection of mouse behavioural anomalies using two-stream convolutional autoencoders. arXiv . ISSN 2331-8422

Full content URL: https://arxiv.org/abs/2106.00598

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Unsupervised detection of mouse behavioural anomalies using two-stream convolutional autoencoders
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Abstract

This paper explores the application of unsupervised learning to detecting anomalies in mouse video data. The two models presented in this paper are a dual stream, 3D convolutional autoencoder (with residual connections) and a dual stream, 2D convolutional autoencoder. The publicly available dataset used here contains twelve videos of a single home-caged mice alongside frame level annotations. Under the pretext that the autoencoder only sees normal events, the video data was handcrafted to treat each behaviour as a pseudo-anomaly thereby eliminating them from the others during training. The results are presented for one conspicuous behaviour (hang) and one inconspicuous behaviour (groom). The performance of these models is compared to a single stream autoencoder and a supervised learning model, which are both based on the custom CAE encoder. Both models are also tested on the CUHK Avenue dataset were found to perform as well as some state-of-the-art architectures.

Keywords:machine learning, unsupervised learning, deep learning, anomaly detection, mouse tracking, animal behaviour
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
G Mathematical and Computer Sciences > G760 Machine Learning
B Subjects allied to Medicine > B140 Neuroscience
D Veterinary Sciences, Agriculture and related subjects > D328 Animal Welfare
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:46515
Deposited On:05 Oct 2021 14:17

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