# SiamCAM: A Real-Time Siamese Network for Object Tracking with Compensating Attention Mechanism
## 4 Experiment
### Analysis  
We evaluate the algorithms in this paper using two small datasets, OTB2015, VOT2018, and one large dataset LaSOT. OTB2015 and VOT2018 have been developed for a long time, have a well-developed assessment system, and have more authoritative assessment data in small datasets. The LaSOT is a large target tracking dataset containing 1400 video sequences, 70 categories, and over 3.52 million precise bounding boxes. Visualizing bounding box annotations makes the LaSOT dataset a large and finely annotated dataset.  
Table 1 illustrates the experimental results of the OTB dataset. As can be seen from Table 1, the algorithm in this paper is the highest in both accuracy and precision on the OTB dataset, surpassing the mainstream depth correlation filtering algorithms. It is nearly 0.01 higher than the baseline algorithm SiamRPN++ and more than 0.02 than  
Figure 6: Comparison of response plots with SiamRPN++ algorithm.  
other mainstream algorithms. The algorithm's performance reaches 0.703 and 0.920 after appropriate tuning of the baseline network parameters. a comparison of the algorithm's performance with the mainstream algorithm after fine-tuning is shown in Figure 7.  
Table 2 shows the comparison results on the VOT2018 dataset. Compared to the baseline network SiamRPN++, the algorithm in this paper improves by nearly 0.02. Due to the difference in experimental equipment and parameter settings, there is an inevitable error in reproducing SiamRPN++ and the original paper. The algorithm for comparison with this paper is the result of our reproduction. However, there is still a gap compared with the mainstream depth correlation filtering algorithms DiMP and ATOM, which have better performance. Compared to OTB2015, the OTB2018 dataset scene is more complex. The related filter series algorithm will perform online fine-tuning during tracking. Its ability to update the image based on its contextualization information during tracking is suitable for objects with similar interference.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Tracker** & **Success** & **Precision** \\ \hline SiamFC [11] & 0.587 & 0.772 \\ MDNet [35] & 0.678 & 0.909 \\ DaSiamRPN [15] & 0.659 & 0.880 \\ ATOM [8] & 0.667 & 0.879 \\ DiMP50 [10] & 0.684 & 0.894 \\ Siam R-CNN [32] & 0.701 & 0.891 \\ SiamRPN++ [18] & 0.696 & 0.910 \\ SA-Siam [36] & 0.657 & 0.865 \\ RASNet [33] & 0.642 & - \\ SCSAtt [39] & 0.641 & 0.855 \\ SiamFRN [38] & 0.636 & 0.840 \\ IMG-Siam [37] & 0.638 & 0.846 \\ \hline Ours & **0.701** & **0.916** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental results of our algorithm and other algorithms on the OTB2015 dataset. The bolded values in the table are the best performance.  
Figure 7: Comparison chart of OTB dataset.  
In addition, the average frame speed of different algorithms on VOT2018 is evaluated in this paper, and the results are shown in Table 3. The algorithm in this paper is better in robustness and other data than the Siamese series papers. The speed is not reduced too much, indicating that this paper can improve the accuracy without sacrificing speed. The comparison between the Siamese family of algorithms and other algorithms also reflects the apparent advantage of Siamese networks in terms of speed, although not accuracy.  
Table 4 illustrates the comparison between the algorithm in this paper and the mainstream algorithm on the LaSOT dataset. From the table, it can be observed that the algorithm of this paper performs well in long sequence videos, and compared with the Siamese network series of algorithms, our algorithm improves the ability of the Siamese network to track targets with severe deformation in videos with a long time and many frames, which further confirms the substantial improvement of the algorithm of this paper in feature discrimination ability.