# Multi-Templates Based Robust Tracking for Robot Person-Following Tasks
## 4 Evaluation
### Public Dataset  
All approaches are further tested on a dataset composed of 27 public datasets involving person tracking. The selected sequences, their source, and the results of tracking robustness are listed in Table 3. The uparrow and downarrow indicate the relative improvement provided by our approach compared to the base approaches. The red, green, and blue denote the methods that ranked first, second, and third in the experimental results, respectively.  
From the table, in most of the subsets, our approach provides extra improvement compared to the base approaches. In general, the mean tracking robustness increases by 14.77% and 10.43% compared to SiamRPN and SiamRPN++, respectively. Furthermore, the improvements are statistically significant. Among all the comparison methods, the average robustness of our proposed approaches ranks first and second respectively. In addition, except for group2_1, our approaches converge to high robustness in all datasets, showing strong target capture ability of our approaches.  
In some subsets, the tracking performance is heavily impacted by the long-term or large-scale occlusion of roofs, buildings, and trees; the anti-occlusion strategy of the aerial viewpoint needs to be further explored.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & \multirow{2}{*}{**Set Name**} & **DaSiamRPN** & **SiamRPN** & **SiamRPN++** & **SiamCAR** & **DiMP** & \multirow{2}{*}{**SiamBAN**} \\  & & **vs.** & **vs.** & **vs.** & **\&** & **\& **\& **\& **SiamBAN** \\  & & **UpdateNet** & **Ours (SiamRPN)** & **Ours (SiamRPN++)** & **SiamGAT** & **PrDiMP** & & \\ \hline \multirow{8}{*}{OTB100} & Girl2 & 9.20/13.53 & 70.60 / 95.67 \(\uparrow\) & 85.27 / 89.67 \(\uparrow\) & 89.53 / 97.20 & 61.47 / 95.40 & 27.73 \\  & Human3 & 2.12 / 2.12 & 76.15 / 93.88 \(\uparrow\) & 5.59 / 94.35 \(\uparrow\) & 27.21 / 99.53 & 2.18 / 90.81 & 2.00 \\  & Human4-2 & 51.57 / 51.57 & 52.02 / 82.31 \(\uparrow\) & 90.85 / 98.50 \(\uparrow\) & 51.72 / 64.92 & 99.85 / 56.52 & 85.61 \\  & Jogging-1 & 98.05 / 98.37 & 95.11 / 100.00 & 96.74 / 96.74 - 96.09 / 97.39 & 98.05 / 99.35 & 91.53 \\  & Walking & 100.00 / 100.00 & 100.00 / 100.00 - & 100.00 / 100.00 - & 99.76 / 99.76 & 99.76 / 99.76 & 99.76 / 99.76 & 99.76 \\  & Walking2 & 55.80 / 55.80 & 65.40 / 76.00 \(\uparrow\) & 67.00 / 72.20 \(\uparrow\) & 99.80 / 53.60 & 50.00 / 72.10 & 53.60 \\  & Woman & 100.00 / 95.81 & 98.49 / 99.33 \(\uparrow\) & 100.00 / 100.00 - & 99.16 / 99.66 & 67.84 / 100.00 & 99.83 \\ \hline \multirow{8}{*}{UAV123} & bike1 & 100.00 / 100.00 & 100.00 / 100.00 - & 100.00 / 100.00 - & 85.67 / 99.97 & 99.97 / 99.97 & 99.97 \\  & group1\_1 & 73.07 / 73.14 & 73.07 / 72.69 \(\uparrow\) & 98.65 / 88.92 \(\uparrow\) & 99.47 / 95.27 & 99.92 / 99.85 & 99.10 \\  & group1\_2 & 54.69 / 81.15 & 56.55 / 94.84 \(\uparrow\) & 34.49 / 98.99 \(\uparrow\) & 94.84 / 99.75 & 99.92 / 99.92 & 81.15 \\  & group1\_3 & 86.96 / 82.85 & 66.83 / 69.81 \(\uparrow\) & 82.71 / 87.03 \(\uparrow\) & 53.65 / 70.80 & 57.69 / 98.37 & 88.80 \\  & group1\_4 & 65.65 / 67.23 & 83.35 / 73.45 \(\downarrow\) & 93.89 / 79.13 \(\downarrow\) & 53.32 / 88.62 & 99.89 / 99.79 & 75.87 \\  & group2\_1 & 10.69 / 10.69 & 10.69 / 10.69 \(\uparrow\) & 10.69 / 10.69 \(\uparrow\) & 10.69 / 10.69 \(\uparrow\) & 10.80 / 10.69 & 10.69 \\  & group2\_2 &