Three-dimensional (3D) paths of resources have been proposed in construction management, as an efficient way for measuring labor productivity. These paths are either extracted by using sensors such as global positioning system (GPS), radio frequency identification (RFID), and ultra-wideband (UWB), or based on cameras placed at jobsites for surveillance purposes. However, the tag-based methods are seriously limited by privacy conflicts since they are not welcome from the personnel. On the other hand, the computer vision based methods have not achieved full automation in measuring labour productivity because they require prior knowledge of the type of tasks performed in specific working zones. This is associated with the lack of depth information. For this purpose, this paper proposes a computationally efficient computer vision method for matching construction workers across different frames. Entity matching is a process that corresponds to a compulsory step prior to the calculation of the 3D position. The proposed matching method, is based on epipolar geometry, template and motion similarity features. The main result of this process is to provide a method for the acquisition of the 3D paths that compose the detailed profile of a construction activity in terms of both time and space.