# Multi-Templates Based Robust Tracking for Robot Person-Following Tasks
## 4 Evaluation
### UGV Dataset
#### 4.1.1 Results and Analysis  
The results are divided into two groups. The short-term group presents the results of the sequences that are less than 100 s. Furthermore, the long-term group gives the results of the rests. Figure 5 illustrates the success plots of short-term tasks. Figure 6 presents the qualitative results of UGV02, UGV03, and UGV14. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Set Name** & **Road Condition** & **Distractor Info** & **Duration** \\ \hline UGV01 & Brick Road & PED & 25 s \\ UGV02 & Brick Road & PED & 19 s \\ UGV03 & Asphalt Road & PED & 20 s \\ UGV04 & Asphalt Road & 1 DAI & 184 s \\ UGV05 & Cement Road & 1 SAI & 325s \\ UGV06 & Asphalt Road & IV,1 SAI & 247 s \\ UGV07 & Asphalt Road & 1 DAI & 423 s \\ UGV08 & Snowy Asphalt Road & 1 DAI,1 SAI & 529 s \\ UGV09 & Grassland & PED & 53 s \\ UGV10 & Grassland & 2 DAI & 76 s \\ UGV11 & Grassland \& Asphalt Road & PED & 301 s \\ UGV12 & Snowy Asphalt Road & PED & 201 s \\ UGV13 & Cement Road & LI & 206 s \\ UGV14 & Cement Road \& Grassland & LI & 82 s \\ UGV15 & Cement Road \& Grassland & LI & 76 s \\ UGV16 & Cement Road \& Grassland & LI,PED & 537 s \\ UGV17 & Cement Road \& Grassland & LI,DAI & 146 s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detailed information of the UGV dataset. For UGV02 and UGV03, we can see that the tracking boxes usually drift when a distracting person passes through and temporarily occludes the target. For UGV14, the variation in light severely impacts the trackers' performance. In general, our approach performs well and provides improvements in both AUC scores and robustness. Figure 7 presents the success plots of long-term following tasks. Our approach ranks high amongst all considered methods. Without fine-tuning the network, the outcomes of  
Figure 5: Success plots of the short-term following. Figure 6: Qualitative results of short-term tasks. DaSiamRPN and UpdateNet are poor on our dataset. Updatenet, which is the update-based variation of DaSiamRPN, does not provide an improvement over DaSiamRPN. Despite the already excellent performance of SiamRPN and SiamRPN++, the AUC score of our approach is improved. The qualitative results are provided in Figure 8.  
In the robot following situations, the robot moves accordingly based on the target location command given by the tracking result: if the tracker tracks the wrong target, the real target will soon be out of view, leading to failure of the following mission. Even though the tracker sometimes does not return a precise bounding box of the following target, a rough result (IOU > 0) can still maintain the target within the tracking view, allowing a chance to recover the target. Therefore, we define tracking robustness as the percentage of the bounding box that satisfies IOU > 0 (namely, the value of the success rate when the overlap threshold = 0 in the success plots). In our opinion, discussing tracking robustness in the following missions is even more meaningful than the AUC score. Since the SiamRPN-based approaches regress bounding boxes from pre-defined anchors, the size adjustment is minor; we demonstrate that the robustness criterion will not be biased by  
Figure 7: Success plots of long-term following. the large-area bounding boxes. As shown in the success plots, our approach provides a substantial gain in terms of tracking robustness compared to the base trackers. For UGV04 (Figure 7a) and UGV07 (Figure 7d), even though the target and distracting person have sufficient disparity in appearance (Figure 8a,d), the tracking box drifts to the distracting person frequently after crossing. By eliminating the responses of the distractors, our approach provides a substantial improvement compared to the other approaches. Figure 8: Qualitative results. For UGV05 and UGV06, the target and distractor person dress similarly (both wearing a black coat), and the following tasks were conducted under intense light variation (see Figure 8b,c). With the light change, the target appearance varies significantly.