Abstract The variation of construction machine poses is one of the main causes for interactive on-site safety issues such as struck-by hazards. With the aim to reduce such hazards, we propose a framework for predicting construction machine poses based on historical motion data and activity attributes. After building a machine motion dataset, we develop a keypoint-based method for recognizing machine activities considering working patterns and interaction characteristics. The recognized activity information is then incorporated with historical pose data to predict future machine poses through a type of recurrent neural network (RNN), named Gated Recurrent Unit (GRU). In experiments of using excavators as the objects, our framework achieves decent performance for machine pose prediction, which is further improved by incorporating activity information, reaching an average percentage of correct keypoints (PCK) of 90.22%. The results indicate the high potential of our framework in predicting construction machine poses and improving on-site safety.

中文翻译：

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使用门控循环单元 (GRU) 考虑历史运动和活动属性的建筑机械姿势预测

摘要 工程机械姿态的变化是造成碰撞危险等交互式现场安全问题的主要原因之一。为了减少此类危险，我们提出了一种基于历史运动数据和活动属性预测建筑机械姿势的框架。在构建机器运动数据集后，我们开发了一种基于关键点的方法，用于考虑工作模式和交互特征来识别机器活动。然后将识别出的活动信息与历史姿势数据结合起来，通过一种称为门控循环单元 (GRU) 的循环神经网络 (RNN) 来预测未来的机器姿势。在使用挖掘机作为对象的实验中，我们的框架在机器姿态预测方面取得了不错的性能，通过结合活动信息进一步改进，达到 90.22% 的正确关键点 (PCK) 的平均百分比。结果表明，我们的框架在预测建筑机械姿势和提高现场安全性方面具有巨大潜力。