通过事件摄像头进行人类姿势估计的联合全球和本地时间建模
Feifan Du1, Zhanpeng Shao1, Xueping Wang1
1College of Information Science and Engineering, Hunan Normal University, 36 Lushan Road, Changsha 410081, China.
Sensors (Basel, Switzerland)
|May 14, 2025
概括
这项研究引入了一种基于事件的人类姿势估计的新型网络,通过结合更长的时间运动线索来提高准确性,以解决静止的身体部位信息丢失的问题.
科学领域:
- 计算机视觉 计算机视觉
- 生物启发的计算技术
背景情况:
- 基于事件的摄像机提供高时间分辨率和动态范围,适合挑战人类姿势估计.
- 现有的方法在事件数据中与静止的身体部位的信息丢失作斗争.
- 短距离的时间分析不足以进行完整的姿势重建.
研究的目的:
- 利用事件摄像头开发一种强大的人类姿势估计方法.
- 为了克服事件流中静止的身体部位缺失数据的挑战.
- 通过利用扩展的时间信息来提高姿势预测的准确性.
主要方法:
- 提出了一个联合的全球和本地时间建模网络 (JGLTM).
- JGLTM从更长的时间范围内提取重要线索.
- 该网络将当地特征与全球时间背景进行精细化,以准确地预测姿势.
主要成果:
- 在基于事件的人类姿势估计中表现出卓越的性能.
- 有效地解决了静止的身体部位信息丢失的问题.
- 在各种场景中实现了准确的姿势预测.
结论:
- 基于事件的JGLTM网络显著改善了基于事件的人类姿势估计.
- 整合较长的时间范围对于处理静止的身体部位数据至关重要.
- 拟议的方法为现实世界的应用提供了一个有前途的解决方案.
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