Muti-view Mouse Social Behaviour Recognition with Deep Graphical Model

TL;DR

This paper introduces a novel deep graphical model that leverages multi-view video data to accurately recognize mouse social behaviors, addressing challenges of view correspondence and data imbalance in rodent behavior analysis.

Contribution

It proposes a multi-view latent-attention and dynamic discriminative model with a variational autoencoder to improve behavior recognition from multi-view videos.

Findings

Outperforms state-of-the-art methods on CRMI13 and PDMB datasets.

Effectively handles imbalanced data in behavior recognition.

Captures view-specific and shared dynamics for accurate analysis.

Abstract

Home-cage social behaviour analysis of mice is an invaluable tool to assess therapeutic efficacy of neurodegenerative diseases. Despite tremendous efforts made within the research community, single-camera video recordings are mainly used for such analysis. Because of the potential to create rich descriptions of mouse social behaviors, the use of multi-view video recordings for rodent observations is increasingly receiving much attention. However, identifying social behaviours from various views is still challenging due to the lack of correspondence across data sources. To address this problem, we here propose a novel multiview latent-attention and dynamic discriminative model that jointly learns view-specific and view-shared sub-structures, where the former captures unique dynamics of each view whilst the latter encodes the interaction between the views. Furthermore, a novel multi-view latent-attention variational autoencoder model is introduced in learning the acquired features, enabling us to learn discriminative features in each view. Experimental results on the standard CRMI13 and our multi-view Parkinson's Disease Mouse Behaviour (PDMB) datasets demonstrate that our model outperforms the other state of the arts technologies and effectively deals with the imbalanced data problem.