This paper presents a significant contribution to the field of repetitive action counting through the introduction of a new approach called Pose Saliency Representation. The proposed method efficiently represents each action using only two salient poses instead of redundant frames, which significantly reduces the computational cost while improving the performance. Moreover, we introduce a pose-level method, PoseRAC, which is based on this representation and achieves state-of-the-art performance on two new version datasets by using Pose Saliency Annotation to annotate salient poses for training. Our lightweight model is highly efficient, requiring only 15 minutes for training on a GPU, and infers nearly 10x faster compared to previous methods. In addition, our approach achieves a substantial improvement over the previous state-of-the-art TransRAC, achieving an OBO metric of 0.56 compared to 0.29 of TransRAC. The code and new dataset are available at https://github.com/MiracleDance/PoseRAC for further research and experimentation, making our proposed approach highly accessible to the research community.

翻译：本文对重复动作计数领域做出了重要贡献，提出了一种名为姿态显著性表示的新方法。所提方法仅使用两个显著姿态而非冗余帧来高效表示每个动作，在提升性能的同时显著降低了计算成本。此外，我们引入了一种基于该表示的姿态级方法PoseRAC，通过使用姿态显著性标注来标注训练中的显著姿态，在两个新版本数据集上达到了最先进的性能。我们的轻量级模型具有极高的效率，在GPU上仅需15分钟训练，推理速度相比先前方法提升近10倍。同时，我们的方法相比先前最先进的TransRAC取得了显著改进，OBO指标达到0.56（TransRAC为0.29）。代码和新数据集已在https://github.com/MiracleDance/PoseRAC 公开，便于研究社区进一步开展实验与探索。