Target tracking algorithms based on deep learning have achieved good results in public datasets. Among them, the network tracking algorithm based on Siamese tracking has a high accuracy and fast speed, which has attracted significant attention. However, the Siamese tracker uses the AlexNet network as its backbone and the network layers are relatively shallow, so it does not make full use of the ability of the deep neural network. If only the backbones of target tracking are replaced, there will be no obvious improvement, such as in the cases of ResNet and Inception. Therefore, this paper designs a wider and deeper network structure. At a wider level, a mechanism that can adaptively adjust the receptive field (RF) size is designed. Firstly, multiple branches are divided by the split operator, and each branch has a different size of kernel corresponding to a different size of RF; then, the fuse operator is used to fuse the information of each branch to obtain the selection weights; and finally, according to the selection, the aggregation feature map is weighted. At a deeper level, a new kind of residual models is designed. The channel is simplified by pruning in order to improve the tracking speed. According to the above, a wider and deeper Siamese network was proposed in this paper. The experimental results show that the structure proposed in this paper achieves a good tracking effect and real-time performance on six kinds of datasets. The proposed tracker achieves an SUC and Prec of LaSOT of 0.569 and 0.571, respectively.

This paper mainly discusses the use of mutual information (MI) and Support Vector Machines (SVMs) for Uyghur Web text classification and digital forensics process of web text categorization: automatic classification and identification, conversion and pretreatment of plain text based on encoding features of various existing Uyghur Web documents etc., introduces the pre-paratory work for Uyghur Web text encoding. Focusing on the non-Uyghur characters and stop words in the web texts filtering, we put forward a Multi-feature Space Normalized Mutual Information (M-FNMI) algorithm and replace MI between single feature and category with mutual information (MI) between input feature combination and category so as to extract more accurate feature words; finally, we classify features with support vector machine (SVM) algorithm. The experimental result shows that this scheme has a high precision of classification and can provide criterion for digital forensics with specific purpose.

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