# Research on the Correlation Filter Tracking Model Based on the Deep-Prunned Feature Network
## 1 Introduction
One of the effective ways to solve the above problems is as follows: Firstly, the pruning algorithm is used to lighten the feature network, to compress the size of the model, reduce the computational burden, and reduce the time it takes for the feature network to extract features, to improve the real-time performance of the correlation filter tracking. Secondly, according to the environment and target changes, the pruned feature network is updated in real-time so that the feature network can adapt to the corresponding changes to improve the tracking accuracy. For online updating of deep convolutional neural networks, common approaches include: fine-tuning the network using the mask of the previous frame sequence [18]; updating the network in a long-term and short-term fashion [19]; capturing changes in the appearance of the target by residual learning and updating the network by a fixed frame interval [20]. Some researchers have also proposed solutions using dynamic networks [21; 22]. Most of the above methods for the online updating of deep convolutional neural networks are proposed for specific disturbances and lack good generalizability and robustness. Therefore, considering the multiple disturbances caused by the environment and updating the deep convolutional neural networks in real-time to improve the tracking accuracy is a bottleneck in the implementation of the deep correlation filter tracking model. Research on convolutional neural network pruning can be divided into fine-grained pruning and coarse-grained pruning. This paper focuses on the coarse-grained pruning  
Figure 1: The general process of deep correlation filter tracking model. task. Coarse-grained pruning is the structured compression of a model at the convolutional kernel or channel level. The core idea is to first evaluate the importance of the convolutional kernels or feature channels. Then, based on a pre-defined pruning rate, all convolutional kernels or feature channels that contribute little to the result are processed using one of three strategies: eliminating all [23]; setting all to zero [24]; or setting all to zero in a non-linear progressive manner [2]. The eliminating all strategy [23] permanently removes the unimportant convolutional kernels in each iteration, which speeds up the training process to some extent, but leads to a great loss of model accuracy. The setting all to zero strategy [24] and setting all to zero in a non-linear progressive manner strategy [2] also prune the convolutional kernels according to their importance. The main difference is that each pruned convolution kernel is still sent into the next training iteration, reducing the accuracy loss of the model. However, the setting all to zero strategy [24] did not consider the association between convolutional kernels and feature maps when measuring the importance of convolutional kernels. Therefore, the setting all to zero in a non-linear progressive manner strategy [2] improved the importance measurement strategy to increase the interpretability of the importance of the convolutional kernels. No matter which strategy is adopted, the above pruning patterns will determine how to adjust and optimize the pruning rate, and how to choose a pruning strategy after the pruning rate is determined to improve the tracking accuracy, while keeping the real-time performance of the model. To solve the bottlenecks mentioned above of the deep correlation filter tracking model, and inspired by the motivation mentioned above and based on our existing research results [1; 2; 3; 4; 24], we propose a \"correlation filter tracking model based on the deep-pruned feature network\" in this paper.