# SiamCAM: A Real-Time Siamese Network for Object Tracking with Compensating Attention Mechanism
## 5 Conclusions  
The Siamese network will track a similarity matching problem of double branch feature with no interaction between the two branch independent structures. We propose a Siamese network architecture based on a compensated attention mechanism to enable feature extraction network processing to extract more discriminative features. We introduce a channel-space attention mechanism to secondary process the features extracted from the search branch by fusing the self-attentive features of the search region and the attention of the template branch selection. Thus, the attention mechanism's erroneous enhancement of the distractors is eliminated, allowing the response value gap between the target and distractor terms to be increased during the later inter-correlation operation. Our Siamese network architecture improves the attention of the feature extraction network to the target features, enabling the entire twin network to improve the discriminative power of the target, achieving a guaranteed real-time speed while improving the tracker performance. However, this new Siamese network still does not solve the problem that the template is stale and cannot adapt to the drastic changes of the target, and how to further improve the ability of the model to judge complex samples will be investigated in the future. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Baseline**} & \multirow{2}{*}{**Base+Attn**} & \multirow{2}{*}{**M**} & \multirow{2}{*}{**A**} & \multirow{2}{*}{**C**} & \multicolumn{3}{c}{**OTB2015**} \\ \cline{5-7}  & & & & & & **Precision** & **Success** \\ \hline \(\surd\) & \(\surd\) & & & & 0.908 & 0.688 \\ \(\surd\) & \(\surd\) & \(\surd\) & & & 0.910 & 0.695 \\ \(\surd\) & \(\surd\) & & \(\surd\) & & 0.915 & 0.698 \\ \(\surd\) & \(\surd\) & & & \(\surd\) & 0.916 & 0.701 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Ablation experiments on dataset OTB2015-2 (Baseline for SiamRPN++ network architecture, Base+Attn: multi-domain attention mechanism applied on two branches of the feature extraction network, M: compensation combination in the form of the dot product, A: weighted, C: connected, the right-hand metric for accuracy). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Baseline**} & \multirow{2}{*}{**C**} & \multirow{2}{*}{**S**} & \multirow{2}{*}{**L1**} & \multirow{2}{*}{**L3**} & \multirow{2}{*}{**L4**} & \multicolumn{3}{c}{**OTB2015**} \\ \cline{5-7}  & & & & & & **Precision** & **Success** \\ \hline \(\surd\) & & & & & & 0.897 & 0.674 \\ \(\surd\) & \(\surd\) & & & & \(\surd\) & 0.889 \(\downarrow\) & 0.670 \\ \(\surd\) & \(\surd\) & \(\surd\) & & & \(\surd\) & 0.899 \(\uparrow\) & 0.681 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & & & 0.895 \(\downarrow\) & 0.673 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & & 0.903 \(\uparrow\) & 0.680 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 0.908 \(\uparrow\) & 0.687 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation experiments on dataset OTB2015-1 (Baseline for SiamRPN++ network architecture, C: channel attention, S: spatial attention,L1, L3, L4: ResNet network Layer1, Layer3, Layer4, respectively; right-hand metric is accuracy). **Author Contributions:** Conceptualization, K.H. and P.Q. ; methodology, K.H. ; software, X.T. ; validation, K.H., P.Q. and X.T. ; formal analysis, L.L. ; investigation, J.C.; resources, K.H. and P.Q. ; data curation, K.H. ; writing--original draft preparation, K.H. ; writing--review and editing, J.C.; visualization, L.L. ; supervision, L.L. and J.C.; project administration, P.Q. ; funding acquisition, L.L. and J.C. All authors have read and agreed to the published version of the manuscript.