# Research on the Correlation Filter Tracking Model Based on the Deep-Prunned Feature Network
## 4 Experiment
### Experimental Results Analysis  
4.1 Comparative Study on Tracking Accuracy and Speed of Pre-Trained Feature Network before and after Pruning  
In this paper, we designed the feature extraction network based on the DCFNet [30] tracking model. To simplify the design process and to illustrate our proposed pruning algorithm, the feature extraction network constructed in this paper has three convolutional layers, each layer with 32 convolutional kernels and a convolutional kernel size of \(3\times 3\). The constructed feature network is used as an un-pruned pre-trained feature network (non-pre-pruned feature network). The VID dataset is used as the pre-training dataset, the error sum of squares of the feature network output response and the desired gaussian response as the loss function. After 50 epochs of training, the non-pre-pruned feature network is obtained. The same dataset and loss function are used, and the PCIP [2] algorithm is used to pre-train and prune the feature extraction network. In this pre-training process, the Temperature parameter T is set to 0.8 and the parameter \(\alpha\), which determines the trend of the penalty coefficient, is set to 10. After 50 epochs of pre-training and pruning, the pre-pruned feature network with three convolutional layers is obtained. Each convolutional layer has 26 convolutional kernels of size \(3\times 3\). To verify the effectiveness of pre-pruning in improving the computational efficiency of the model, the tracking speed and tracking accuracy of the pre-trained feature network with pre-pruning are compared with those of the pre-trained feature network without pruning in the actual tracking process. Object tracking of video sequences in the OTB2013 dataset is performed using Formulas (15)-(19), and the results are shown in Figure 8 and Table 1.  
As shown in Figure 8 and Table 1, analyzing the tracking speed (FPS), tracking accuracy (average pixel error (APE)), and tracking success rate (average overlap rate (AOR)) can be seen. During the tracking process, although the APE of the pre-pruned feature network decreases by 0.01 and the AOR decreases by 0.08 compared to the non-pre-pruned feature network, the tracking speed increases by about 8 FPS. In practical object tracking applications, the relationship between tracking speed and tracking accuracy is contradictory, so it is common to increase the tracking speed as much as possible while ensuring tracking accuracy. Therefore, we use the PCIP algorithm to prune the feature network, which leads to a decrease in the tracking accuracy of the pre-pruned feature network, but still maintains 99% of the original model tracking accuracy level, and the improvement in tracking speed is of great significance for tasks such as object tracking that require high real-time performance. 4.2 Comparative Study on Tracking Accuracy and Speed of Pre-Pruned Feature Network and Deep-Pruned Feature Network  
The global pruning rate \(GTPR\) is determined using the Formulas (5)-(8) before each video sequence in the OTB2013 dataset is tracked. The deep-pruned feature network is obtained by further pruning the pre-pruned feature network based on the global pruning rate \(GTPR\), and its model size is further simplified. To evaluate the performance difference, based on Equations (15)-(19) and using the pre-pruned feature network and the deep-pruned feature network for object tracking of each video sequence in the OTB2013 dataset, respectively, the tracking results of both are shown in Figure 9 and Table 2.  
From the tracking results shown in Figure 9 and Table 2, it can be seen that the APE of the tracker decreased by 0.01 and the AOR decreased by 0.47 for the deep-pruned feature network compared to the pre-pruned feature network, and the tracking speed improved by about 23 FPS.