# Research on the Correlation Filter Tracking Model Based on the Deep-Prunned Feature Network
## 6 Conclusions  
This paper proposes a correlation filter tracking model based on the deep-pruned feature network, which reduces the model complexity of the feature network and improves the tracking speed of the tracker while maintaining the object representation capability of the deep convolutional neural network and ensuring the tracking accuracy of the tracker when environment changes take place. Firstly, based on the pre-pruning network obtained by PCIP, an optimal global tracking pruning rate (\(GTPR\)) is determined in terms of the contribution of filter channels to tracking response. Secondly, based on (\(GTPR\)), an alternative convolutional kernel is defined to replace non-important channel kernels and maximally compensates for the accuracy loss of the feature network, which leads to the further pruning of the feature network. Thirdly, when large changes in the tracking scene are detected using the SSIM criterion during the actual tracking process, we adopt a strategy of online updating of the feature network,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Training Option** & **Non-Online-Updating** & **Online-Updating** \\ \hline AOR & 52.82 & 55.82 \\ \hline APE & 0.70 & 0.73 \\ \hline Speed & 97.60 & 84.80 \\ \hline \hline \end{tabular}
\end{table}
Table 4: AOR and APE, as well as speed of online-updating or non-online-updating, on the OTB2013 dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Training Option** & **Non-Pre-Pruned** & **Pre-Pruned** & **Deep-Pruned** & **Deep-Pruned with Alternative Kernels** & **Online-Updating** \\ \hline AOR & 52.50 & 52.42 & 51.95 & 52.82 & 55.82 \\ \hline APE & 0.70 & 0.69 & 0.68 & 0.70 & 0.73 \\ \hline Speed & 68.50 & 76.30 & 99.50 & 97.60 & 84.80 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of all experimental results.  
which effectively improves the adaptability of the feature network to changes in the tracking scene, and thus improves the tracking accuracy of the tracker. The experimental results based on the OTB2013 dataset illustrate the effectiveness of the proposed method in this paper. This paper provides an effective method for applying the deep correlation filter tracking model to real-world applications and deploying it to embedded and mobile devices with limited computational and memory resources. The implementation of this method on embedded mobile devices is the future major research work.  
Conceptualization, H.C. and C.L.; Methodology, H.C. and C.L.; Visualization, H.C.; Writing--original draft, H.C.; Writing--review and editing, H.C. and C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 62006257), and the National Key R&D Program of China (Grant 2020 YFB1406702-3).  
Not applicable.  
The raw data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.  
The authors would also like to thank [PERSON] for his valuable comments.  
The authors declare no conflict of interest.