## 3 Dataset and Experimental Environment Construction
### Establishment of Experimental Environment  
To compare the actual training and recognition effects of the models, the original model needs to be modified for experimental comparison. The performance parameters of different structural models can be obtained by training the recompiled models. Network training is divided into blur image processing module and gestures target detection module. This experiment is based on the Win10 system. The experiment hardware configuration is shown in Table 1. The experimental effect is more significant with a higher configuration. The Pychram software is used to compile the model. The Anaconda software is used to manage Python and install third-party libraries. By calling different libraries, the configuration of model training environment is configured. The model is compiled by  
Figure 10: Motion blur image processing renderings: (**a**) original motion blur image in dataset; (**b**) motion blurred image processed by Wiener filtering; (**c**) motion blurred image processed by Wiener filtering and bilateral filtering.  
using a highly modular Tensorflow framework. In order to make the experimental results more comparable, each different structural model is trained in the same environment. The results were compared. The verification environment of experiment is shown in Table 2. The parameters of the training network are shown in Table 3.  
Based on VOC format datasets, the training of the neural network is executed. The verification datasets are static gesture images collected by the rear camera of the mobile phone in common use scenarios. Four different gestures are defined, they are forward, stop, victory, and holding. Each gesture has 100 pictures. The picture size is 1080\"1438. Each gesture image set accounts for no less than 50% of the blurred images. The three gesture original datasets total 400. After data enhancement processing, the final dataset number is 200 images of each gesture. The final dataset is 800 in total. In general, 80% of the dataset is used to implement the network training. In total, 20% of the dataset is employed to verify the effect of network training. The annotation of the dataset is completed by using Labelimg software. The annotation diagram of the dataset is shown in Figure 11. The.xml file is generated by selecting the target gesture in the picture and entering the gesture category. After all the datasets are marked, the.xml file is converted into a.txt file, which contains the image path, target category, and location information of the target gesture. The.xml file can be utilized in the later training of network. In Figure 11, the original pictures in the dataset are annotated by Labelimg software to generate the.xml files. Among them, the original image is given in Figure 11a. The image after labeling the target gesture through Labelimg software is given in Figure 11b. The.xml file generated after image annotation is given in Figure 11c. The.xml file contains the information such as the reading route of the files, the size of the pictures, the target category, the location of the target boxes, and the sizes of the target boxes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**System** & \multicolumn{2}{c}{**Central Processing Unit**} \\ \hline Win10-64 bit & Intel(R) Core (TM) i7-5500U CPU @ 2.40 GHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experiment hardware configuration.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & \multicolumn{2}{c}{**Value**} \\ \hline Batch size & 2 \\ Learning rate & 0.001 \\ Init\_epoch & 0 \\ Freeze\_epoch & 50 \\ Unfreeze\_epoch & 100 \\ Fixed image size & 416*416 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training network parameters.