# Deep Clustering Efficient Learning Network for Motion Recognition Based on Self-Attention Mechanism
## 4 Experiment
### Experimental Results and Analysis
#### 4.2.2 Network Model Performance Comparison Module  
We have the following steps to train our network model, which incorporates the self-attention mechanism. We pretrained the self-attention modules of people and their body parts to ensure convergence. We will verify the pedestrian behavior detection in real life. The whole training process includes three steps: pretraining LSTM network, extracting behavior features through the self-attention mechanism, and finally sending the feature layer to the network. The importance of each person in each frame is generated by the self-attention mechanism, and then the behavioral characteristics of each person are summed according to the weight, the weighted features are input into the LSTM network, and the output features are classified to jointly predict the detection, individual and collective behaviors. In order to better test the pedestrian's motion information in the video, we connected the self-attention mechanism with the features extracted from the original RGB trajectory. The parameters of the network are fixed to train the network of individual behavior characteristics. In each time step, we organized the focused character features into subgroups and then input them into the network module to generate context features circularly. These features were cascaded and transmitted to the final LSTM network, followed by the softmax classification layer, which enables the entire context network to be end-to-end trained without any additional coding steps. In all experiments, we set the trade-off parameters to 1 and 2, respectively, and used the random gradient descent of the Adam optimizer to obtain the best parameters of the model. The output dimension was set to 10, and the initial learning rate was set to 0.002. We also compared our method with the following latest research models, as shown in Table 2: The accuracy rate means that all the predicted samples are correct (for the detection of human movement behavior, the final result prediction is correct), and all the predicted experimental samples are divided. The recall rate is to predict positive samples as positive samples (that is, the detection of human movement behavior is correct) and divide the results that are originally correct. We show the results of the pedestrian activity dataset in Figure 6.  
The pedestrian activity dataset contains 50 video sequences of various group activities, including the location of pedestrians and their collective and individual behavior tags. Each person's behavior is identified and detected. Table 2 shows the results of the comparison. Obviously, this method is superior to other networks listed, which shows that our framework is feasible and can improve \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{4}{c}{**MNIST**} \\ \cline{2-5}  & **KMeans [40]** & **AE [41]** & **DBSCAN [42]** & **DEC** \\ \hline ACC & 0.52 & 0.78 & 0.69 & 0.95 \\ NMI & 0.53 & 0.77 & 0.82 & 0.92 \\ ARI & 0.34 & 0.69 & 0.71 & 0.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Indicators for comparing different clustering algorithms on MNIST dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Accuracy\%** & **Recall\%** \\ \hline [PERSON] et al. [2] & 91.1 & 91.2 \\ [PERSON] et al. [3] & 91.6 & 91.5 \\ [PERSON] et al. [4] & 92.2 & 92.3 \\ [PERSON] et al. [5] & 93.4 & 93.4 \\ [PERSON]. [PERSON] et al. [6] & 93.6 & 93.7 \\ [PERSON] et al. [13] & 90.8 & 90.8 \\ [PERSON] et al. [16] & 95.3 & 95.4 \\ [PERSON] et al. [17] & 95.6 & 95.8 \\
**ours** & **96.1** & **96.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of pedestrian activity detection indicators. performance. The combination of the two further improves accuracy. This shows that DEC and the self-attention mechanism play a key role. At the same time, our results are better than the traditional deep learning model. It can be seen that important people and body parts received more attention.