# Tracking System for a Coal Mine Drilling Robot for Low-Illumination Environments
## 4 Experimental
### Experiments in Low-Illumination Environments  
We acquired an image sequence in an underground garage to determine the performance of the proposed algorithm under low-illumination conditions. The video was 34 s long and contained 1044 frames. The target was subjected to light changes, out-of-plane rotation, a similar background, and an almost completely dark area starting in the 800 th frame. The effect of illumination discrimination on image enhancement is shown in Figure 9. A bright spot occurs in the lower-left corner of the image in the 83 rd frame, resulting in a global brightness value of 120, although the brightness around the target is only 21. The BB discrimination performs well for image enhancement; the global illumination is 120 in the 83 rd frame. The standard _David_ sequence is calibrated starting in the 300 th frame, but the target is already visible in the 150 th frame (_Light_ = 30). This sequence indicates that the threshold \(T_{L}\) does not need to be very high. However, the disadvantage is observed in the _Garage_ sequence (Figure 3). In Figure 6, which depicts the _Garage_ sequence after the 800 th frame, the global illuminance is at the maximum, but the BB illuminance is less than 20. The target cannot be distinguished from the surrounding environment. The exposure control parameter \(k\) was set to 1, and the lighting intensity threshold \(T_{L}\) was conservatively set to 48. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Attribute** & **LLCT** & **LRCF** & **BACF** & **f-DSST** & **LCT** \\ \hline SV & 0.555 & 0.524 & 0.598 & 0.517 & 0.479 \\ ARC & 0.510 & 0.475 & 0.548 & 0.477 & 0.442 \\ LR & 0.527 & 0.488 & 0.520 & 0.464 & 0.420 \\ FM & 0.373 & 0.354 & 0.493 & 0.343 & 0.276 \\ ROC & 0.387 & 0.378 & 0.425 & 0.383 & 0.382 \\ POC & 0.530 & 0.473 & 0.555 & 0.466 & 0.462 \\ OV & 0.420 & 0.407 & 0.511 & 0.415 & 0.398 \\ BC & 0.491 & 0.453 & 0.513 & 0.444 & 0.478 \\ IV & 0.495 & 0.482 & 0.510 & 0.482 & 0.439 \\ VC & 0.506 & 0.486 & 0.571 & 0.493 & 0.444 \\ CM & 0.551 & 0.497 & 0.618 & 0.494 & 0.491 \\ SOB & 0.654 & 0.624 & 0.668 & 0.601 & 0.581 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The average precision on UAV123. Figure 9: Comparison of global and BB illumination. The top figure shows the luminance values of the _David_ sequence, and the following is the _Garage_ sequence. The BB and global illumination increase over time in the David sequence. However, in the _Garage_ sequence, the global brightness is always the maximum because of the lamp on the right. Figure 10 presents the comparison of the two modes of LIME, the MATLAB function _imadjust_, and the accelerated version, in terms of the time cost. The first graph in Figure 10 presents the comparison curves of the four solvers (accelerated version, _imadjust_, LIME-exact, and LIME-speed solvers) in terms of time cost. The sample image is scaled to different levels from the original size (3144 \(\times\) 3078). The results show that the four solvers are sufficiently efficient when the image size is smaller than 400. However, the histogram indicates that the LIME solvers (the exact solver and the speed solver) require several seconds to calculate a frame when the image size is small. In contrast, the proposed method requires only milliseconds to perform an iterative calculation. For a 720P resolution image, the accelerated version is 20 times faster than the accelerated LIME version, and it can run at around 60 FPS. Our method is slightly faster than the MATLAB function. When the image size exceeds 1000, there is a sharp difference in the time cost between the four enhancement methods. The frame rate of our method remains on the order of milliseconds, whereas LIME requires seconds. Thus, the accelerated version of the image enhancement algorithm has a low computational cost, meeting the real-time requirements of mobile robots equipped with 720P resolution cameras. Figure 11 presents the comparison of our tracker (yellow) with the benchmark trackers, e.g., LRCF [17] (blue), fast DSST [39] (green), and LCT [18] (red).