# SiamCAM: A Real-Time Siamese Network for Object Tracking with Compensating Attention Mechanism
## 4 Experiment
### Ablation Experiments  
Ablation experiments were performed on the OTB2015 dataset for different components and combinations of approaches, and SiamRPN++ was chosen for the base network. To avoid different feature extraction networks being affected by different parameters and network layers, the ablation experiments of the attention mechanism were all performed on the same feature extraction network. ResNet is selected here. Table 5 shows the ablation experiments of applying different types of attention to the feature extraction network and adding attention at different positions. The attention mechanism is combined in a default weighted approach.  
The performance of the tracker is degraded by first using channel attention directly on Layer4 of ResNet. The different layers are tested, and the reasons for failure are analyzed. It is analyzed that SiamRPN++ already suppresses the category that is not the target when performing the deep inter-correlation operation. This operation plays a specific role in channel selection, so there is no corresponding effect of further enhancement of the channel of the target response.  
Then our algorithm combines different types of attention mechanisms in the channel and spatial domains, and the enhancement of the target features is somewhat improved. The tracking effect is also improved, and the accuracy is increased from 89.7 to 89.9. However, the effect was slight. After experimenting with different network layers for analysis, the analysis concluded that ResNet has deep network layers. The features extracted from different layers are different types of image features. The lower layer features are more of some region, line, and color features. In comparison, the deeper layer features are more oriented to get the semantic information of the image, which is why the feature pyramid can achieve better results in combining different layers of features. We analyzed the effect of feature information noticed by different features layers on the experimental results and found that deeper semantic information was more effective than shallow feature attention. Therefore, we improved the performance of the tracker to 90.8 after fusing the information  
Figure 8: Visualization of our algorithm compared with other state-of-art algorithms.  
of different layers which combined the attentional enhancement of Layer1, Layer3, and Layer4. The experimental results are shown in Table 5.  
After we determined that the combined attention mechanism was effective for feature extraction, we conducted ablation experiments on the combined way of compensating attention, as shown in Table 6.  
Three combinations of template branch and search branch attention were selected for experimentation: weighted, dot product, and connected. The connection achieved the best effect, which improved the tracker performance by almost one percentage point to 91.6; the other two methods also improved the experimental results, 91.0 and 91.5, respectively. It shows that the feature attention of the template branch does have a compensating effect on the search branch.