## 1 Introduction
The market proposes some commercial solutions for virtual guidance, such as Bosch's Active Assist system ([[https://www.boschrexroth.com/en/xc/products/product-groups/assembly-technology/news/activeassist-assistance-system/index/](https://www.boschrexroth.com/en/xc/products/product-groups/assembly-technology/news/activeassist-assistance-system/index/)]([https://www.boschrexroth.com/en/xc/products/product-groups/assembly-technology/news/activeassist-assistance-system/index/](https://www.boschrexroth.com/en/xc/products/product-groups/assembly-technology/news/activeassist-assistance-system/index/)), accessed on 20 January 2022), Arkite HIM ([[https://arkite.com/product/](https://arkite.com/product/)]([https://arkite.com/product/](https://arkite.com/product/)), accessed on 20 January 2022), Rhinoassembly Light Guide System ([[https://www.rhinoassembly.com/en/catalog/product/-Light-Guide-LGS-LGS/](https://www.rhinoassembly.com/en/catalog/product/-Light-Guide-LGS-LGS/)]([https://www.rhinoassembly.com/en/catalog/product/-Light-Guide-LGS-LGS/](https://www.rhinoassembly.com/en/catalog/product/-Light-Guide-LGS-LGS/)), accessed on 20 January 2022) and Vir.GIL ([[https://www.comau.com/it/competencies/digital-initiatives/technologies/vir-gil/](https://www.comau.com/it/competencies/digital-initiatives/technologies/vir-gil/)]([https://www.comau.com/it/competencies/digital-initiatives/technologies/vir-gil/](https://www.comau.com/it/competencies/digital-initiatives/technologies/vir-gil/)), accessed on 20 January 2022) (Figure 1). They are powerful and flexible systems that are able to guide the operator during the training of a new assembly, inspection or maintenance procedure by means of digital information projected on a workbench or directly on the asset to be manipulated. However, such innovative products do not implement algorithms that are able to accurately track the pose of a hand tool that might also be partially occluded by the operator's hands. In fact, in the best case, these industrial solutions roughly track the position of the hand center and assume that a certain task has been performed if the hand center position is enough close to a specified area. This can clearly lead to a rough performance evaluation and the learning of bad habits. In order to make a difference, it is fundamental to track, with high accuracy, the pose of the tools the worker handles during the learning session of the procedure [12]. As an example, if a worker is required to tighten several screws in sequence, an evaluation of whether the screws were tightened following the correct sequence without missing any screws might be requested. The problem of the pose estimation of 3D objects, including the hand tool for allowing autonomous manipulation [13], has been studied for years and is an open and debated problem. The approaches that allow for high accuracies rely on a 3D model of the object that has to be detected within a 3D point cloud [14]. However, such methods are time consuming and may not be applicable due to the unavailability of the 3D model [14]. The unstoppable rise of deep learning in recent years has pushed the pose estimation research out of the the boundaries of classical computer vision techniques, and new approaches based on deep neural networks have been exploited [15]. Novel methods require minimal human intervention, improve the performance of 3D data-based approaches [16; 17] and, in some cases, avoid the usage of 3D models for estimating the pose of an object [18; 19]. To the best of the authors' knowledge, even if the problem has also been extended to the general situation of occluded objects, hand tool pose estimation and tracking in the industrial environment during assembly and maintenance procedures has not been extensively investigated. Detecting and tracking a tool handled by an operator performing an assembly or maintenance procedure is not easy, mainly because of the tool's occlusion due to the operator's hands.