Counting repetitive actions is important in work and daily life. Automated counting using deep learning provides a more efficient, accurate alternative to manual counting, which is tedious and error-prone Deep-learning models have been proposed to automatically count repetitive actions in video content. However, for these models to be applied to realistic scenes, high-quality performance and generalization to multiple environments, particularly for long videos, are essential. To address these challenges, we propose a new model, ME-RAC, which includes the multipath 3D-Conv encoder module, and we also propose a temporal-sequence random-combination data augmentation to improve counting performance and prevent model over-fitting during training. Additionally, we propose the temporal-sequence-decision (TSD) framework system to realize long repetitive-action counting in complex realistic scenes. We conducted experiments to validate that our proposed methods perform better than comparable methods and our TSD framework achieved unique performance in long repetitive-action-counting tasks.

中文翻译：

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用于重复动作计数的多路径 3D-Conv 编码器和时间序列决策

计算重复动作在工作和日常生活中很重要。使用深度学习的自动计数为手动计数提供了更高效、更准确的替代方案，手动计数既繁琐又容易出错。深度学习模型已被提出来自动计数视频内容中的重复动作。然而，为了将这些模型应用于现实场景，高质量的性能和对多种环境的泛化，特别是长视频，是至关重要的。为了应对这些挑战，我们提出了一种新模型 ME-RAC，其中包括多路径 3D-Conv 编码器模块，我们还提出了一种时间序列随机组合数据增强，以提高计数性能并防止训练期间模型过度拟合。此外，我们提出了时间序列决策（TSD）框架系统来实现复杂现实场景中的长时间重复动作计数。我们进行了实验来验证我们提出的方法比同类方法表现更好，并且我们的 TSD 框架在长时间重复动作计数任务中实现了独特的性能。