# Research on the Correlation Filter Tracking Model Based on the Deep-Prunned Feature Network
## 4 Experiment
### Experimental Results Analysis
The performance difference mentioned above is due to the fact that the secondary pruning further compresses the network structure based on the initial pruning, which weakens the target characterization ability of the feature network and results in a decrease in the accuracy of the tracker. However, the advantage of the deep-pruned feature network is that it further reduces the computational load of the model and improves the speed of the tracker. Therefore, secondary pruning of the pre-pruned network results in a loss of accuracy of about 0.01, but in exchange for a speedup of almost 23 FPS. Thus, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Training Option** & **Non-Pre-Pruned** & **Pre-Pruned** \\ \hline AOR & 52.50 & 52.42 \\ \hline APE & 0.70 & 0.69 \\ \hline Speed & 68.50 & 76.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: AOR and APE, as well as speed of pre-pruned feature network or non-pre-pruned feature network, on the OTB2013 dataset. Figure 8: Success and precision plots on OTB2013 dataset with pre-pruned feature network or non-pre-pruned feature network. From the tracking results shown in Figures 9 and 10, and Tables 2 and 3, it can be seen that the introduction of alternative kernels to the feature network leads to a decrease in tracking speed of about 2 FPS, which is about 21 FPS higher than that of the pre-pruned feature network, and about 29 FPS higher than that of the non-pre-pruned feature network. As for the tracking accuracy, the deep-pruned feature network with alternative kernels has a 0.02 improvement compared with the deep-pruned feature network, and can basically keep the same accuracy level as the non-pre-pruned feature network. Therefore, it can be concluded that the introduction of the alternative kernels can, on the one hand, maintain the performance improvement of the pruned feature network in terms of tracking speed; and on the other hand, it can improve the robustness of the feature network and effectively improve the accuracy loss caused by pruning. 4.4 Comparative Study on Tracking Accuracy and Speed of Feature Network with or without Online Updating  
In the actual tracking process, to monitor whether a large scene change has occurred in the current video sequence, we start from the second frame of the video sequence and use Equation (20) to calculate the SSIM of the current frame and the previous frame, and set the threshold value to 0.5. When SSIM is greater than the threshold value, it is considered that the current tracking scene has not significantly changed and there is no need to update the feature network; when SSIM is less than the threshold value, it is considered that  
As can be seen from the tracking results shown in Figure 11 and Table 4, the online updating for the feature network causes the speed of the tracker to be around 85 FPS, which is about 13 FPS lower compared to the non-online-updating strategy, but still about 16 FPS higher compared to the non-pre-pruned feature network. In terms of tracking accuracy, the tracking accuracy of the online updated feature network is 0.73, which is 0.03 higher than that of the non-pre-pruned feature network. The above results show that when the adaptability of the feature network to scene changes decreases, online updating can effectively improve its adaptability to scene changes while maintaining the improvement of tracking speed, thus improving the overall tracking performance of the tracker.