LAViTSPose:一个轻量级的级联框架,通过检测-细分-分类来实现稳健的坐姿识别.
Shu Wang1,2, Adriano Tavares2, Carlos Lima2
1School of Computer Science, Zhuhai College of Science and Technology, Zhuhai 519041, China.
Entropy (Basel, Switzerland)
|December 24, 2025
概括
LAViTSPose准确地识别坐姿,即使有遮和有限的数据. 这种轻量级的框架可以在现实环境中改善人体工程学评估和健康监测.
科学领域:
- 计算机视觉 计算机视觉
- 人与计算机的交互
- 人体工程学就是人体工程学.
背景情况:
- 准确的坐姿识别对于人体工程学和健康监测至关重要.
- 现实世界的挑战,如闭包和有限的注释数据,阻碍了模型的性能.
- 现有的方法在关键点错位和概括方面扎.
研究的目的:
- 开发一个强大而轻量级的框架,用于在具有挑战性的现实场景中识别坐姿.
- 为了解决封闭,注释稀缺和跨领域概括的问题.
- 提高自动化人体工程学评估的准确性和效率.
主要方法:
- 拟议的LAViTSPose是一个级联框架,使用基于YOLOR的探测器与距离感知 IoU (RaIoU) 损失来改进人体检测.
- 集成的ESBody处理跨人泄漏和估计闭塞/头部定向线索.
- 开发了一个紧的视觉变压器 (ViT) 头 (MLiT),具有空间位移接触 (SDC) 和可学习温度 (LT),用于基于骨架的分类和结构一致性的规范化.
主要成果:
- 在USSP数据集上,LAViTSPose在坐姿分类和面部定向识别方面表现出色.
- 该框架有效地处理部分可见性和注释稀缺性.
- 实现了实时推断速度,使其适合实际应用.
结论:
- LAViTSPose在坐姿识别方面取得了重大进展,特别是在闭塞和有限数据条件下.
- 拟议的方法提高了特征的可区分性,并减少了结构.
- 这一框架有望改善大规模的人体工程学评估和健康监测系统.
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