# RMFNet: Redetection Multimodal Fusion Network for RGBT Tracking
## 6 Conclusions  
This article proposes a new robust RGBT tracking network called the Redetection Multimodal Fusion Network (RMFNet). RMFNet uses a three-branch structure to fully exploit the potential value of shared and specific information in multimodal data. By combining medium-term feature-level fusion with in a multimodal fusion approach, RMFNet fully utilizes the complementary advantages of multimodal information and compensates for  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **MDNet + RGBT** & **MANet** & **DAFNet** & **CBPNet** & **RMFNet-noAR** & **RMFNet** \\ \hline \multirow{2}{*}{GTOT} & Speed & 3 fps & 1.1 fps & 2 fps & 3.7 fps & 14.2 fps & 10.9 fps \\  & PR/SR & 80.0/63.7 & 89.4/72.4 & 88.2/70.7 & 88.5/71.6 & 87.7/71.5 & 91.1/76.9 \\ \hline \multirow{2}{*}{RGBT234} & Speed & 3 fps & 1.5 fps & 2 fps & 3.3 fps & 13.8 fps & 10.1 fps \\  & PR/SR & 72.2/49.5 & 77.7/53.9 & 76.6/53.7 & 79.4/54.1 & 78.2/54.4 & 79.4/58.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation results for runtime and overall performance.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Method**} & \multicolumn{4}{c}{**RGBT234**} & \multicolumn{4}{c}{**GTOT**} \\ \hline  & **Baseline** & **LF** & **FB** & **EFM** & **RD** & **AR** & **PR** & **SR** & **PR** & **SR** \\ \hline I & ✓ & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 75.5 & 51.9 & 83.6 & 68.2 \\ II & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & 76.2 & 52.9 & 86.5 & 70.0 \\ III & ✓ & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) & 77.2 & 53.5 & 87.4 & 70.3 \\ IV & ✓ & ✓ & ✓ & ✓ & \(\times\) & \(\times\) & 77.4 & 54.0 & 87.7 & 71.1 \\ V & ✓ & ✓ & ✓ & ✓ & ✓ & \(\times\) & 78.2 & 54.4 & 87.7 & 71.5 \\ VI & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & **79.4** & **58.5** & **91.1** & **76.9** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The validity of the components was verified in the GTOT and RGBT234 datasets.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**RGBT234**} & \multicolumn{4}{c}{**GTOT**} \\ \cline{2-6}  & **PR** & **SR** & **PR** & **SR** \\ \hline Baseline & 75.5 & 51.9 & 83.6 & 68.2 \\ Baseline + EFM-AFF & 71.8 & 46.0 & 82.5 & 64.7 \\ Baseline + EFM & 77.9 & 52.9 & 86.9 & 69.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: EFM integration strategy analysis evaluation results.  
the shortcomings of single-modality information. Additionally, RMFNet uses an improved re-detection algorithm to address the failure accumulation problem of the base algorithm, as well as a multi-stage bounding box estimation strategy further to improve the robustness and accuracy of the tracker. Through extensive experiments, RMFNet has demonstrated good performance in various challenging scenarios and has stable tracking capabilities. In the future, we will further investigate how to leverage the temporal and motion information of videos to improve RMFNet and enhance the performance of the tracker.  
Y.Z.: Investigation, Methodology, Validation, Writing--original draft. H.L.: Resources, Writing--review and editing. G.G.: Funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This study is supported by the Natural Science Foundation of China (Grant Nos. U1903213).  
Not applicable.  
Data Availability Statement: Data presented in this study are available upon request from the corresponding author.  
The authors declare no conflict of interest.