## 2 Related Work
Moreover, these methods tend to accumulate error over time (drift) and face difficulties when the background is cluttered. Various authors have proposed the incorporation of the color information (e.g., tcl-histogram data), as well as new optimization techniques; among these, the PW3P3D method [30] or the work by [PERSON] et al. [17] stand out. Some of the most common tracking by detection methods used by the scientific community use the local appearance features of an image, such as the SIFT method [26]. As previously mentioned, this tracking technique is not recommended in industrial environments, as the objects are not usually textured. Another way to estimate the pose of a 3D object is by using an RGB-D or depth camera and calculate the local 3D geometric descriptors of the 3D model, such as Point Pair Features (PPFs) [27]. The main problem with these methods is that they tend to be very computationally expensive [15; 35], preventing their use in AR systems. Another alternative is using template matching techniques, such as the LINEMOD method [15]. The main limitation of these kind of methods is that are occlusion-sensitive [31]. Recently, methods for detecting and estimating the pose of object based on deep-learning techniques have been introduced. Such methods have gained great popularity in recent years, mainly due to their promising results. Some of these current methods are BB8 [31], SSD-6D [32], or the most recent DPOD [33]. Despite their promising results, these methods have not yet been used in an AR system applied to industrial maintenance [33], except for a few exceptions, such as the work of [34] who proposed an AR system applied to assembly. These new methods have mostly only been tested on pre-established public data sets, such as LINEMOD [15] or T-LESS [36]. This is mainly because they need many real images in which the real pose of the different objects of interest has been labeled or an accurate 3D model of the object. Moreover, some of these methods suffer when the tracked target is occluded, and may not be fast enough for an AR system [31; 32; 33; 34], greatly limiting their real application in the industry. At present, IRT is a mature technology, which can be used to monitor thermal conditions in real time and without contact. Thermography allows for the detection of equipment failures during operation, thus providing a significant reduction in downtime and associated maintenance costs [37]. Its use has been extended to different applications, including the monitoring of civil structures, electrical installations, machinery, and equipment; as well as in different industries, such as nuclear, aerospace, or food [38]. IRT has been successfully applied to the maintenance of electrical installations--mainly for the detection of faults in static machines, such as power transformers--although it has also been applied to \begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{2}{c}{**Markerless**} \\ \hline \multicolumn{2}{c}{} & \multicolumn{2}{c}{Model-based} \\ \cline{3-4} \multicolumn{2}{c}{} & \multicolumn{1}{c}{} & Tracking by detection \\ \multicolumn{2}{c}{} & \multicolumn{1}{c}{} & \(\bullet\) Local appearance features [26] \\ \multicolumn{2}{c}{} & \multicolumn{1}{c}{} & \(\bullet\) Local 3D geometric descriptors [27] \\ \multicolumn{2}{c}{} & \multicolumn{1}{c}{} & \(\bullet\) Template-based [15] \\ \multicolumn{2}{c}{} & \multicolumn{1}{c}{} & \(\bullet\) Deep-learning methods [31; 32; 33; 34] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main AR tracking methods. the predictive maintenance of rotating electrical machines. The authors of [38] proposed a monitoring methodology based on IRT, to detect failures in induction motors, such as misalignments, cooling problems, imbalances between phases, or bearing damage. Its effectiveness was validated through the analysis of several practical cases. The results obtained in these investigations demonstrated an accuracy of 91% in the detection of failures and operating anomalies of the equipment monitored with infrared thermography.