SqueakPose Studio:一个端到端的平台,用于姿势估计和实时边缘AI部署
bioRxiv : the preprint server for biology
|February 9, 2026
概括
我们开发了一个用于姿势估计的集成系统,使得边缘设备上的实时动物行为分析成为可能. 这个平台简化了数据收集,模型培训和部署,而不需要强大的工作站.
科学领域:
- 行为神经科学 行为神经科学
- 计算生物学是一种计算生物学.
- 机器学习 机器学习
背景情况:
- 准确的姿势估计对于定量行为分析至关重要.
- 当前的深度学习工具通常需要复杂的离线工作流程和专门的硬件.
- 由于软件碎片化和硬件限制,实时部署姿势估计模型具有挑战性.
研究的目的:
- 呈现一个集成的软件硬件生态系统,用于位估计.
- 为了实现从数据集创建到边缘设备上的实时部署的无过渡.
- 克服现有的离线中心姿势估计工具的局限性.
主要方法:
- 开发了SqueakPose Studio用于数据集创建,标记,训练和离线推理.
- 利用现代物体检测架构进行高效的端到端训练和推理.
- 集成的SqueakView和MouseHouse可在嵌入式边缘计算硬件上实时部署.
主要成果:
- 该系统支持跨CPU,GPU和Apple Silicon的高效姿势估计.
- 启用实时模型部署,视频捕获和边缘设备上的传感器日志.
- 通过共享数据格式和确定时间,确保线下分析和实时部署之间的一致性.
结论:
- SqueakPose生态系统为线下和实时的姿势估计提供了一个统一的平台.
- 方便嵌入式边缘计算设备的行为分析,而不依赖于工作站级硬件.
- 推进深度学习的可访问性和应用,用于实验环境中的定量行为分析.
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