  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 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. Both models are also tested on the CUHK Avenue dataset were found to
perform as well as some state-of-the-art architectures.
