# Deep Clustering Efficient Learning Network for Motion Recognition Based on Self-Attention Mechanism
## 5 Discussion  
In the detection and recognition of human behavior, our method achieved good results. Previous researchers improved the YOLOv5 algorithm to detect abnormal human behavior. This method adds a shielding convolutional attention model to the original YOLOv5 backbone network. The module starts with a shielding convolutional layer, and the central area of the receptive field is covered by the nucleus. By predicting the total opening of the screen credit, the error related to the shielding information is used as the abnormal score. At the same time, the Swin CA module [43] is embedded in the detection network. By learning the features of adjacent layers, the model can better grasp the global information, thus reducing the impact of background information on the  
Figure 8: The confusion matrix of two data sets. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Accuracy\%** & **Recall\%** \\ \hline [PERSON] et al. [2] & 91.2 & 91.2 \\ [PERSON] et al. [3] & 91.7 & 91.5 \\ [PERSON] et al. [4] & 92.3 & 92.3 \\ [PERSON] et al. [5] & 93.4 & 93.2 \\ [PERSON] et al. [6] & 93.5 & 93.6 \\ [PERSON] et al. [13] & 90.8 & 90.9 \\ [PERSON] et al. [16] & 94.3 & 94.4 \\ [PERSON] et al. [17] & 96.6 & 96.8 \\
**ours** & **97.5** & **97.5** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison results of football datasets. detection results. By extracting the scale features of abnormal human behavior in different backgrounds, the complexity of the whole model calculation is reduced, and the accuracy of the model for locating the abnormal human behavior target is improved. However, there are still the following shortcomings: due to the different scenarios collected by different data sets, the characterization of human abnormal behavior is also different in different scenarios, making this method less versatile; secondly, the training of the model needs to label the detected image in advance, and the workload is large in the early stage; and finally, the method is not sensitive to the continuity of human actions in the image, which makes the judgment of human abnormalities in the detection process appear with corresponding delay or false detection and missing detection, which is worthy of our attention and improvement. In addition, some researchers use the lightweight network MobileNet v2 to replace the original feature extraction network VGG-16, use the deformable convolution module to build a convolution layer to enhance the receptive field, and then integrate the location information into the channel attention to enhance the feature, which can capture the remote dependency between the spatial positions so as to better handle the overlapping occlusion problem. This method mainly focuses on the problems faced by the crowd abnormal behavior detection algorithm. Aiming at the high complexity of the existing model algorithm, it replaces the feature extraction network with the lightweight network MobileNet v2, thereby reducing the model parameters and improving the model running speed. In order to solve the problem of low detection accuracy in complex scenes, such as overlapping occlusion, deformable convolution is used to change the feature extraction method, and the attention mechanism is added to enhance the features. By learning the context relationship, the occlusion part is predicted, thus effectively solving the occlusion problem. Although this method is capable of application, it is particularly insufficient in the face of large-scale data sets. The method proposed by us can solves such problems and continues to be verified in future work. However, our method also has the following shortcomings: at present, we only carried out experiments on a single football dataset, and it has not been popularized to other large-scale sports datasets. The scenarios used are also different, and the qualitative detection of athletes' behavior is also varied in different scenarios, so the universality of this method is relatively lacking.