## 4 Experimental Results
### Performance Evaluation  
The main idea proposed in this paper is to recognize action at a rapid rate through the spatiotemporal feature vector (\(FV_{K}\)) generated by weighted mean of the spatial feature map (\(FM_{K}^{N}\)) extracted from the frame stack (\(FS^{N}\)) consisting of sequential images with the change rate (\(FD^{N}\)) of each frame representing time information. The performance evaluation is conducted by comparing the action recognition accuracy and complexity of the HAR model using the proposed weighted mean and the existing HAR models. The comparison results of performance evaluation are shown in Table 2, and results are obtained using workstation. Models used for comparison of performance evaluation received RGB stream exactly as the system was built in this paper, and complexity of model and the average accuracy were compared according to the three random scenarios of the train-test split list provided by UCF-101. Equation (2) represents the complexity of each model, and the definition of each factor is defined in Table 3. If the network of the model is 3D CNN, time-axis operation is added to the convolution operation. The recognition accuracy of 3D CNN and single-stream CNN was 84.8%, 85.2%, and 88.1%, respectively, and the recognition accuracy of the model using the LSTM-based method was 90.8% and 91.21%. The LSTM-based action recognition model shows higher accuracy than 3D CNN and single-stream CNN because sequential data are received and weighted to identify connectivity. However, the proposed model based on single-stream CNN recognized action with 2.48% higher accuracy than deep LSTM. This study proved that the proposed method using the change rate of sequential frames as time information can effectively identify the connectivity of motion for action recognition. \[N_{\text{CNN}}=\sum_{conv}2C_{k-1}C_{k}N_{k}^{(kernel)}N_{k}+\sum_{pool}C_{k}N _{k}\Big{(}N_{k}^{(pool)}-1\Big{)}+2(C_{k}N_{k}N_{d}+N_{d}N_{out})\]  
\[N_{LSTM}=L\cdot\sum_{k=1}^{K}(8(N_{k-1}+N_{k})N_{k}+4N_{k})+2N_{K}N_{out} \tag{2}\]  
\[N_{BILSTM}=L\cdot\sum_{k=1}^{K}(8(N_{k-1}+N_{k})N_{k}+4N_{k})+2N_{K}N_{out}\]  
Figure 5: The proposed Jetson NANO-based HAR system appearance. In addition, additional experiments for performance evaluation were conducted to verify the applicability of the action recognition model on embedded systems equipped with low-cost GPUs. Experiments were conducted by means of \"Element-wise Mean,\" \"Weighted Mean,\" \"ConvLSTM,\" and \"Bi-ConvLSTM.\" The \"Element-wise Mean\" means an operation that averages \(FM_{K}^{N}\) outputted from CNN according to the number of \(N\) without weighting time information. The frame number (\(N\)) of \(FS^{N}\) extracted according to a random interval is increased in five intervals from 10 to 40, the action recognition accuracy was measured, and testing time times were compared according to the frame number (\(N\)) of \(FS^{N}\), and then testing time was measured by the average time it takes to output the action class from each video of the test data set. The action recognition accuracy and testing time of three methods according to \(N\) number of \(FS^{N}\) extracted from the video data according to any interval are shown as a graph in Figure 6 and results are obtained using Jetson NANO. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Network** & **Accuracy (\%)** & **Complexity** \\ \hline Multi-scale CNN [46] & 3D CNN & 84.80 & \(N_{CNN}\) \\ C3D [47] & 3D CNN & 85.20 & \(N_{CNN}\) \\ MSD [48] & 3D ConvLSTM & 90.80 & \(N_{CNN}+N_{LSTM}\) \\ FCN [49] & Single-stream CNN & 88.10 & \(N_{CNN}\) \\ Deep LSTM [50] & 2D ConvLSTM & 91.21 & \(N_{CNN}+N_{BLSTM}\) \\ Ours & Single-stream CNN & 93.69 & \(N_{CNN}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of average accuracy of the proposed method for UCF-101 with other methods. Figure 6: Accuracy and testing time according to frame number of \(FS^{N}\) extracted at a random interval: (**a**) accuracy and (**b**) testing time.