InfoGCN++:通过预测在线骨架式动作识别的未来来学习表示
IEEE transactions on pattern analysis and machine intelligence
|September 26, 2024
概括
InfoGCN++通过从当前和未来的运动中学习,实现基于骨的实时动作识别,克服了InfoGCN.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 像InfoGCN这样的基于骨架的动作识别模型可以实现高精度,但需要完全观察动作.
- 这种限制限制了它们在实时应用中的使用,例如监视和机器人技术.
- 由于需要完整的序列数据,现有的方法难以识别在线动作.
研究的目的:
- 推出InfoGCN++,InfoGCN的扩展,用于在线基于骨架的动作识别.
- 为了实现实时的动作分类,无论观察序列的长度如何.
- 开发一个从当前学习并预测未来运动的模型,以实现强大的行动代表性.
主要方法:
- InfoGCN++扩展了InfoGCN架构,用于在线行动识别.
- 它将预测视为基于观察到的行动的推断问题.
- 纳入神经常规微分方程 (NODE) 来建模隐藏状态的持续演变.
主要成果:
- 在三个基准标准中,InfoGCN++在在线行动识别方面表现出色.
- 达到与现有最先进技术相等或超过的性能.
- 成功实现了独立于序列长度的实时动作分类.
结论:
- 在线基于骨架的动作识别方面,InfoGCN++对InfoGCN来说是一个显著的进步.
- 模型从未来的运动中学习的能力提高了实时识别能力.
- InfoGCN++有可能对监控,机器人和人机交互中的实时应用产生重大影响.
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