# Research on the Correlation Filter Tracking Model Based on the Deep-Prunned Feature Network
## 2 Related Works  
### Deep Convolutional Neural Network Pruning  
As mentioned above, structured coarse-grained pruning is the structured compression algorithm, which takes convolutional kernels or channels as the pruning unit to implement the structured compression of a deep convolutional feature network at the convolutional kernel or channel level, without introducing additional sparsity into the network. However, it is inevitable for fine-grained pruning to produce sparsity. It is difficult to train a good pruning model due to sparsity [24]. In addition, it is necessary for dealing with the sparsity to have extra hardware support [25] which is an extra workload for network pruning. Therefore, structured coarse-grained pruning algorithms have gradually become a major research direction in the field of deep convolutional network pruning research in recent years.  
[PERSON] et al. [23] proposed a CNN acceleration method based on convolutional kernel pruning, using the L1 norm of the matrix to measure the importance of each convolutional kernel and remove the convolutional kernels with low importance, and the corresponding feature maps as a way to reduce the computational cost of the CNN. [PERSON] et al. [26] used the scaling factor \(\gamma\) in Batch-Normalization as a parameter to measure channel importance and added a regular term of \(\gamma\) to the objective function to be able to automatically prune during the network training process. [PERSON] et al. [27] proposed an OT (Optimal Thresholding) pruning algorithm, considering the differences between different convolutional layers and weight distributions. OT algorithm prunes unimportant channels by calculating the optimal threshold layer by layer, which avoids the problem of over- or under-pruning. In most of the pruning strategies described above, the mode of \"eliminating all\" unimportant convolutional kernels or channels is used, which will inevitably lead to a large loss of model accuracy. To address this problem, [PERSON] et al. [24] did not use the \"eliminating all\" strategy when pruning unimportant convolutional kernels, but set the kernels that needed to be pruned in this iteration to zero and continued training these kernels in the next iteration. The obvious advantage of this is that it considers the global information of a single convolutional kernel during the training process, rather than relying solely on the training results of a particular iteration to assess its final degree of importance. The method proposed by [PERSON] et al. [24] improves the accuracy loss due to \"eliminating all\", but the shortcoming is that the importance of the convolutional kernel is measured solely by the information of the kernel itself, without considering the connection between the kernel and the feature map. In addition, the \"set all to zero\" strategy causes each iteration of training to start from zero, which may affect the computational efficiency of the training process. [PERSON] et al. [2] improved the algorithm based on [24] and proposed the PCIP (pruning deep feature networks using channel importance propagation) pruning algorithm. In this study, we use the \"set all to zero in a non-linear progressive manner\" strategy to prune the unimportant convolutional kernels, which not only reduces the accuracy loss caused by pruning, but also improves the computational efficiency during the pruning training process. Additionally, the global average pooling of the feature map is used as the basis for measuring the importance of convolutional kernels, which considers the correlation between convolutional kernels and the feature map, and improves the interpretability of the importance of convolutional kernels.