Skeleton-based action recognition has made significant advancements recently, with models like InfoGCN showcasing remarkable accuracy. However, these models exhibit a key limitation: they necessitate complete action observation prior to classification, which constrains their applicability in real-time situations such as surveillance and robotic systems. To overcome this barrier, we introduce InfoGCN++, an innovative extension of InfoGCN, explicitly developed for online skeleton-based action recognition. InfoGCN++ augments the abilities of the original InfoGCN model by allowing real-time categorization of action types, independent of the observation sequence's length. It transcends conventional approaches by learning from current and anticipated future movements, thereby creating a more thorough representation of the entire sequence. Our approach to prediction is managed as an extrapolation issue, grounded on observed actions. To enable this, InfoGCN++ incorporates Neural Ordinary Differential Equations, a concept that lets it effectively model the continuous evolution of hidden states. Following rigorous evaluations on three skeleton-based action recognition benchmarks, InfoGCN++ demonstrates exceptional performance in online action recognition. It consistently equals or exceeds existing techniques, highlighting its significant potential to reshape the landscape of real-time action recognition applications. Consequently, this work represents a major leap forward from InfoGCN, pushing the limits of what's possible in online, skeleton-based action recognition. The code for InfoGCN++ is publicly available at https://github.com/stnoah1/infogcn2 for further exploration and validation.

翻译：基于骨架的动作识别近期取得了显著进展，诸如InfoGCN等模型展现了卓越的准确性。然而，这些模型存在一个关键局限：它们在分类前需要完整观察动作过程，这限制了其在监控系统和机器人系统等实时场景中的应用。为突破这一障碍，我们提出InfoGCN++——作为InfoGCN的创新扩展，专为在线骨架动作识别而设计。InfoGCN++通过允许独立于观察序列长度进行实时动作类型分类，增强了原始InfoGCN模型的能力。它超越传统方法，通过从当前动作和预测的未来动作中学习，构建对整个序列更全面的表征。我们将预测视为基于已观测动作的外推问题。为此，InfoGCN++引入神经常微分方程（Neural Ordinary Differential Equations），使其能够有效建模隐藏状态的连续演化。经过在三个骨架动作识别基准上的严格评估，InfoGCN++在在线动作识别中展现出卓越性能。它持续达到或超越现有技术，凸显其重塑实时动作识别应用格局的巨大潜力。因此，本工作标志着从InfoGCN的重大突破，推动了在线骨架动作识别领域的可能性边界。InfoGCN++的代码已在https://github.com/stnoah1/infogcn2 公开，供进一步探索与验证。