Innovate Spatial-Temporal Attention Network (STAN) for Accurate 3D Mice Pose Estimation with a Single Monocular RGB Camera

Liyun Gong, Miao Yu, Gautam Siddharth Kashyap, Sheldon McCall, Mamatha Thota, Saeid Pourroostaei Ardakani

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

Abstract

Precise 3D pose estimation of mice holds crucial importance across various scientific domains. In this research, we introduce an innovative model named the Spatial-Temporal Attention Network (STAN), specifically designed for accurate 3D pose estimation of mice using a single monocular camera. The STAN model leverages a sequence of extracted 2D skeletons to predict the 3D pose of a mouse. Through the incorporation of spatial and temporal attention modules, our STAN methodology adeptly captures intricate spatial and temporal relationships among key points, thereby enabling a comprehensive representation of the dynamic movements inherent in a mouse's behavior for precise 3D pose estimation. To assess the effectiveness of our proposed method, extensive experimental evaluations were undertaken. The results show the superior performance of the STAN model when compared to other state-of-the-art approaches within the realm of 3D mouse pose estimation.

Original languageEnglish
Title of host publication32nd European Signal Processing Conference, EUSIPCO 2024 - Proceedings
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages616-620
Number of pages5
ISBN (Electronic)9789464593617
DOIs
Publication statusPublished - 2024
Externally publishedYes
Event32nd European Signal Processing Conference, EUSIPCO 2024 - Lyon, France
Duration: 26 Aug 202430 Aug 2024

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491

Conference

Conference32nd European Signal Processing Conference, EUSIPCO 2024
Country/TerritoryFrance
CityLyon
Period26/08/2430/08/24

Keywords

  • computer vision
  • deep learning
  • mice 3D pose estimation
  • multi-head attention
  • temporal/spatial information
  • transformer

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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