In construction fields, it is common for workers to rely on hand signals to communicate and express thoughts due to their simple but effective nature. However, the meaning of these hand signals was not always captured precisely. As a result, construction errors and even accidents were produced. This paper presented a feasibility study on investigating whether the hand signals could be captured and interpreted automatically with computer vision technologies. It starts with the literature review of existing hand gesture recognition methods for sign language understanding, human-computer interaction, etc. It is then followed by creating a dataset containing 11 classes of hand signals in construction. The performance of two state-of-the-art 3D convolutional neural networks is measured and compared. The results indicated that a high classification accuracy (93.3%) and a short inference time (0.17 s/gesture) could be achieved, illustrating the feasibility of using computer vision to automate hand signal recognition in construction.

在建筑领域，由于其简单而有效的性质，工人通常依靠手势进行交流和表达思想。但是，这些手势的含义并不能始终被准确地捕捉到。结果，产生了施工错误甚至事故。本文提出了一项可行性研究，以调查使用计算机视觉技术是否可以自动捕获和解释手势。首先是对现有手势识别方法的文献综述，以进行手语理解，人机交互等。然后，创建一个包含11种类别的手势信号的数据集。测量并比较了两个最新的3D卷积神经网络的性能。