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 20 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。该方法摒弃了冗余帧，仅利用两个显著性姿态即可高效表征每个动作，从而在提升性能的同时显著降低计算成本。此外，我们提出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对训练中的显著性姿态进行标注，在两个新版本数据集上达到了最先进的性能。我们的轻量级模型高效性显著，在GPU上仅需20分钟即可完成训练，且推理速度较先前方法提升近10倍。另外，我们的方法相较于之前最先进的TransRAC取得了实质性提升，OBO指标达到0.56，而TransRAC仅为0.29。代码和新数据集已在https://github.com/MiracleDance/PoseRAC 公开，便于研究社区进行进一步研究与实验。