# RMFNet: Redetection Multimodal Fusion Network for RGBT Tracking
## 5 Performance Evaluation
### Evaluation on the RGBT234 Dataset  
We also verified the performance of our RMFNet with other trackers on a larger-scale dataset, RGBT234, to further validate its effectiveness. The evaluation results on the RGBT234 dataset are analyzed for three aspects, overall performance, performance under 12 challenging attributes, and visualization results in some highly challenging scenarios. The overall performance of RMFNet was compared with 11 RGB trackers and 14 RGBT trackers. The RGB trackers are MDNet [12], RT-MDNet [33], DAT [37], ECO [30], SOWP [43], SRDCF [36], CSR-DCF [44], DSST [45], CFnet [32], SAMF [46], and C-COT [47]. The RGBT trackers are KCF + RGBT [28], SiamDW + RGBT [34], MDNet+RGBT [12], RT-MDNet + RGBT [33], SGT [38], JSR [48], L1-PE [49], MANet [13], DAPNet [1], HDINet [42], JMMAC [40], APFNet [4], and MACNet [41]. As shown in Figure 8a, RMFNet outperforms all RGB trackers, especially compared to DAT and ECO, where RMFNet achieves 6.3%/8.2% and 9.2%/7.1% performance improvements on PR/SR, respectively. As shown in Figure 8b, although RMFNet performs 3.3% worse than APFNet on PR, it performs 0.6% better on SR. The RGBT234 dataset comprises 12 challenge attributes, low resolution (LR), partial occlusion (PO), scale variation (SV), background clutter (BC), thermal crossover (TC), deformation (DEF), low illumination (LI), camera motion (CM), motion blur (MB), no occlusion (NO), fast motion (FM), and heavy occlusion (HO). We selected 12 trackers from the aforementioned RGB and RGBT trackers for comparing the results of 12 challenging attributes. As depicted in the evaluation results shown in Figures 9 and 10, RMFNet outperforms \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Attributes** & **OCC** & **LSV** & **FM** & **LI** & **TC** & **SO** & **DEF** & **ALL** \\ \hline
**ECO** & 77.5/62.2 & 85.6/70.5 & 77.9/64.5 & 75.2/61.7 & 81.9/65.3 & 90.7/69.1 & 75.2/59.8 & 77.0/63.1 \\
**RT-MDNet** & 73.3/57.6 & 79.1/63.7 & 78.1/64.1 & 77.2/63.8 & 73.7/59.0 & 85.6/63.4 & 73.1/61.0 & 74.5/61.3 \\
**SiamDW+RGBT** & 67.5/53.6 & 68.9/56.6 & 71.1/57.6 & 70.0/58.8 & 63.5/51.7 & 76.4/58.5 & 69.1/58.2 & 68.0/56.5 \\
**SRDCF** & 72.7/58.0 & 80.4/68.1 & 68.3/61.1 & 71.7/59.4 & 70.5/58.0 & 80.5/57.5 & 66.6/53.7 & 71.9/59.1 \\
**RT-MDNet+RGBT** & 79.6/61.8 & 80.9/63.6 & 79.4/61.2 & 85.0/68.9 & 86.4/65.8 & 93.5/67.6 & 90.8/73.4 & 83.9/66.9 \\
**MDNet+RGBT** & 82.9/64.1 & 77.0/57.3 & 80.5/59.8 & 79.5/64.3 & 79.5/60.9 & 87.0/62.2 & 81.6/73.3 & 80.0/63.7 \\
**SGT** & 81.0/56.7 & 84.2/54.7 & 79.9/55.9 & 88.4/65.1 & 84.8/61.5 & 91.7/61.8 & 91.9/73.3 & 85.1/62.8 \\
**CMR** & 82.5/62.6 & 85.3/66.7 & 83.5/65.0 & 88.7/67.8 & 81.1/62.2 & 86.5/61.0 & 84.7/65.2 & 82.7/64.3 \\
**MANet** & 88.2/69.6 & 86.9/70.6 & 87.9/69.4 & 91.4/73.6 & 88.9/70.2 & 93.2/70.0 & 92.3/75.2 & 89.4/72.4 \\
**DAPNet** & 87.3/67.4 & 84.7/54.8 & 82.3/61.9 & 90.0/72.2 & 89.3/69.0 & 93.7/69.2 & 91.9/77.1 & 88.2/70.7 \\
**MACNet** & 87.6/68.7 & 84.6/67.3 & 82.3/65.9 & 89.4/73.1 & 89.2/69.7 & 95.0/69.5 & 92.6/76.5 & 88.0/71.4 \\
**APFNet** & 90.3/71.3 & 87.7/71.2 & 86.5/68.4 & 91.4/74.8 & 90.4/71.6 & 94.3/71.3 & 94.6/78.0 & 90.5/73.7 \\ \hline
**RMFNet** & 89.2/73.6 & 88.1/75.1 & 85.9/73.4 & 92.5/77.1 & 92.0/76.3 & 93.6/74.9 & 94.1/78.4 & 91.1/76.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: PR/SR scores (%) for RMFNet and 12 trackers on 7 challenge attributes in the GTOT dataset. The best, second, and third results are denoted by red, green, and blue, respectively. other trackers in most of these challenges. In PR evaluation, RMFNet performs well in BC, CM, DEF, LI, and FM challenges, indicating a stable performance in these challenging scenarios. However, in the HO, SV, and NO challenges, RMFNet's performance is inferior to that of MACNet, as MACNet makes better use of appearance information than RMFNet. Moreover, RMFNet is outperformed by HDINet in LR and PO challenges, implying that our model needs improvement in utilizing complementary information.