# Research on the Correlation Filter Tracking Model Based on the Deep-Prunned Feature Network
## 2 Related Works
### Online Updating of Feature Network  
The feature network of the deep correlation filter tracking model is usually obtained by offline pre-training, and the pre-trained feature network is not modified during the whole tracking process. For more stable tracking scenarios, not modifying the feature network during the tracking process will not have a significant impact on the performance of the tracker. However, in the practical application of object tracking, the tracking scene often changes with factors such as illumination and the motion state of the target. Offline pre-trained feature networks have difficulty adapting to such changes. Similarly, the pruned network based on the offline pre-trained feature network is also difficult to adapt to such changes in the tracking environment, and therefore cannot accurately extract target features, resulting in the performance degradation of the tracker. Therefore, to improve the ability of the feature network to adapt to complex scene changes, one of the effective ways is to optimize the parameters of the feature network through an online update. [PERSON] et al. [19] used two strategies to update the network online. The first was to action a long-term update of the network after each fixed frame interval. The second was to action a short-term update of the network when the failure of tracking was detected. [PERSON] et al. [20] proposed the CREST (convolutional residual learning scheme for visual tracking) algorithm, which captures changes in target appearance in a residual learning manner and updates the network every fixed frame interval. It effectively improves the robustness of the network when the appearance of the target significantly changes. [PERSON] et al. [28] used \"first frame updating\", \"interval frame updating\", and \"failure updating\" to update the network. That is, using the first frame so the network initially learns the characteristics of the current target; using the high-score samples of frames with several intervals to update the network; and using high-score samples to update the network when the tracking fails, which can improve the characterization ability of the network. Most of the above methods use fixed intervals to update the network. Although they can partially solve the network adaptability problem, the algorithm lacks flexibility and universality, and cannot completely solve the actual adaptability problem in the tracking process. Unlike fixed-interval updating strategies, [PERSON] et al. [3] used SSIM to determine the similarity of tracking boxes in two consecutive frames in real-time during the tracking process, and only updated the feature network when SSIM was below a certain threshold. This not only considers the disturbing factors that cause the decrease in tracking accuracy more comprehensively and reduces the calculation workload, but also considers the actual changes in the tracking process and ensures the robustness of the model. Although the abovementioned research on network pruning have provided effective pruning strategies, there is a lack of sufficient research on how to determine the pruning rate and how to choose the pruning strategy after the pruning rate is determined to improve the tracking accuracy of the model, while keeping the real-time performance of the model. To address these issues, based on our existing research results of network online updatingand channel-importance-propagation-based deep feature network pruning [1; 2; 3; 4; 24], we conducted a study on the \"correlation filter tracking model based on the deep-pruned feature network\". The main research is as follows:  
1. Based on our existing research results of channel-importance-propagation-based deep feature networks Pruning (PCIP), we implement a pre-pruning of the feature extraction network;
2.