Innovate Spatial-Temporal Attention Network (STAN) for Accurate 3D Mice Pose Estimation with a Single Monocular RGB Camera
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 skeleton 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.
History
School affiliated with
- School of Computer Science (Research Outputs)
Publication Title
32nd European Signal Processing Conference (EUSIPCO 2024)Publisher
IEEEISSN
2219-5491eISSN
2076-1465ISBN
979-8-3315-1977-3eISBN
978-9-4645-9361-7Date Accepted
2024-05-23Date of Final Publication
2024-10-23Event Name
32nd European Signal Processing Conference EUSIPCO 2024Event Dates
26 - 30 August 2024Open Access Status
- Not Open Access
Publisher statement
© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Will your conference paper be published in proceedings?
- Yes