The use of skeletal data allows deep learning models to perform action recognition efficiently and effectively. Herein, we believe that exploring this problem within the context of Continual Learning is crucial. While numerous studies focus on skeleton-based action recognition from a traditional offline perspective, only a handful venture into online approaches. In this respect, we introduce CHARON (Continual Human Action Recognition On skeletoNs), which maintains consistent performance while operating within an efficient framework. Through techniques like uniform sampling, interpolation, and a memory-efficient training stage based on masking, we achieve improved recognition accuracy while minimizing computational overhead. Our experiments on Split NTU-60 and the proposed Split NTU-120 datasets demonstrate that CHARON sets a new benchmark in this domain. The code is available at https://github.com/Sperimental3/CHARON.

使用骨骼数据，通过连续学习的上下文来高效有效地进行动作识别。我们引入了CHARON（连续人类动作识别骨骼网络），通过均匀采样、插值和基于掩模的内存高效训练阶段等技术，在保持一致性能的同时，在一个高效的框架内操作，实现了提高的识别准确性，同时最小化计算开销。我们在Split NTU-60和提出的Split NTU-120数据集上的实验表明CHARON在该领域树立了新的基准。

面具与压缩：持续学习中高效基于骨架的动作识别