# Fast and Accurate Visual Tracking with Group Convolution and Pixel-Level Correlation
## 4 Experiments
### Results on GOT10K and LaSOt  
As a large tracking dataset, GOT10K contains more than 10,000 videos, and it is populated with more than 560 categories of moving objects and 87 motion patterns--more than other datasets. We tested our model on the test set. As shown in Table 4, compared with SiamCAR [39], SiamFC++ [6], and Ocean [36], our tracker achieves an AO of 60.7%, which is 1.2% better than that of SiameseFC++ and generally better than that of the other trackers.  
LaSOt contains 70 object categories and provides an equal number of sequences for each category to mitigate potential category bias, resulting in a collection of 1400 sequences with an average video length of 2512 frames, constituting a high-quality tracking dataset. We tested our tracker on this test set. As shown in Table 5, our tracker outperforms Ocean by 1.2% and has a better performance than the other trackers, which shows its effectiveness and generalizability.  
Figure 12 shows that our model can track successfully in the face of size variation, occlusion, and low resolution, improving the success and precision rates. The inaccuracy  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **SiamFC** & **SiamRPN** & **SiamRPN++** & **SiamCAR** & **SiamFC++** & **Ocean** & **Ours** \\ \hline AO & 0.374 & 0.483 & 0.517 & 0.569 & 0.595 & 0.611 & 0.607 \\ \(SR_{0.5}\) & 0.404 & 0.581 & 0.616 & 0.670 & 0.695 & 0.721 & 0.713 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison with other trackers on the GOT10k test set.  
Figure 11: (**a**) Overall success and precision plots of our tracker on OTB100 compared with other trackers. (**b**) Success plot for visual attributes. (**c**) Precision plot for visual attributes.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **SiamRPN++** & **SiamCAR** & **SiamBAN** & **CGCAD** & **PGNet** & **Ocean** & **Ours** \\ \hline UAV123 & 0.611 & 0.604 & 0.615 & 0.623 & 0.619 & 0.621 & 0.655 \\ OTB100 & 0.695 & 0.696 & 0.696 & 0.691 & 0.703 & 0.676 & 0.701 \\ LaSOt & 0.469 & 0.507 & 0.514 & 0.518 & 0.531 & 0.560 & 0.572 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison with other trackers on the UAV123, OTB100, and LaSOt datasets in terms of the AUC.  
of the boat tracking is due to the fixed viewpoint, and as the boat is traveling from far to near, its size changes rapidly, so the tracker does not work well. Our model aggregates multi-layer features with different receptive fields, which reduces the problem of accuracy degradation due to the change in the size of the object. The person tracking inaccuracy is due to the close distance and high similarity of the two people, resulting in the bounding box containing both. Pixel-level correlation is a more refined correlation method that can refine the bounding box and diminish tracking exceptions caused by background interference. Due to the small size and fast movement of UAVs, tracking errors often occur. The attention module can enhance the feature extraction ability of the network, allowing the network to focus on important features and track successfully. Therefore, our tracker provides a better accuracy than the other algorithms in different situations. Meanwhile, compared to SiamCAR's inference speed of 31 FPS, our model runs at 40 FPS, representing an improvement of 9 FPS, which is an improvement in both speed and success.