## 3 Proposed Method
The accuracy and loss curve for the YouTube actions dataset are depicted in Figure 2:5 Furthermore, we present the difference between the corrected and predicted labels of the UCF11 dataset. The confusion matrix of the UCF11 or \begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Accuracy\%** \\ \hline Hierarchical clustering [14] & 89.7\% \\ Single-stream CNN [72] & 93.1\% \\ BT-LSTM [16] & 85.3\% \\ KFDI [17] & 79.4\% \\ Dilated CNN+BiLSTM+RB [18] & 89.01\% \\ Local–global features+QSVM [19] & 82.6\% \\
3 DCNN [21] & 85.2\% \\ Deep autoencoder+CNN [22] & 96.2\% \\ ViT-ReT [20] & 92.4\% \\ Two-stream LSTM [25] & 89.20\% \\ \hline Proposed method & 99.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison analysis of the proposed model with SOTA methods by using UCF11 or YouTube Actions dataset. YouTube actions dataset is depicted in Figure 7. This matrix provides an overview of the accuracy at the class level. The \"basketball\", \"biking\", \"golf swing\", \"horse riding\", \"soccer juggling\", \"tennis swing\", \"trampoline jumping\", \"volleyball\", and \"spiking\", in these classes, achieved remarkable accuracy near 100%. The diving, swing, and walking classes showed low accuracy of 96%, 97%, and 96%, respectively. It is worth noting that certain action classes, such as volleyball spiking, basketball, and shooting, exhibited similar motions, which consequently minimized the occurrence of errors in classification. Figure 6: Accuracy and loss curve of the proposed model for UCF11 dataset. Figure 7: The confusion matrices attained through the utilization of the proposed model on the UCF11 dataset concerning the true and predicted labels. ### Performance Accuracy and SOTA Comparison with UCF Sports Dataset

The quantitative comparison analysis of the proposed model with SOTA methods by using the UCF Sports dataset is shown in Table 2. The methods QST-CNN-LSTM [23], GMM + KF + GRNN [24], two-stream LSTM [25], and dilated CNN+BiLSTM+RB [18], achieved accuracy rates of 93.2 %, 89.1%, 89.0%, and 92.2%, respectively. The proposed model achieved effective results for the UCF Sports dataset. We achieved nearly 100% for all classes except for the running class, which was due to model confusion between the diving running and kicking running classes due to the limited contextual alteration between them. The outcomes of the UCF Sports dataset in terms of the confusion matrix and



### Performance Accuracy and SOTA Comparison with JHMDB Dataset

The quantitative comparative results for the JHMDB dataset and other methodologies are given in Table 3. We compared our proposed model to the challenging JHMDB dataset to enhance the performance of our proposed model in identifying demanding actions such as brushing, catching, clapping, golfing, jumping, kicking, pouring pulling, shooting a ball, shooting bowing, and throwing. Our model demonstrated a recognition accuracy exceeding 90% for actions such as catching, sitting, playing basketball, and running. The model accomplished an impressive overall classification level of 76.6% for the challenging actions in the J-HMDB dataset, thus substantiating the significance and effectiveness of the model. Figure 10 demonstrated the curve of the accuracy and loss. \begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Accuracy\%** \\ \hline Deep networks [26] & 67.24\% \\ Hybrid deep neural network [27] & 69.0\% \\ Multi-stream CNN [28] & 71.17\% \\ DD-Net [15] & 65.0\% \\ HAR-Depth [29] & 73.1\% \\ Two-stream LSTM [25] & 52.70\% \\ Proposed method & 76.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison analysis of the proposed model with SOTA methods by using the JHMDB dataset. Figure 10: Accuracy and loss curve of the proposed model for JHMDB dataset. Figure 9: Accuracy and loss curve of the proposed model for UCF Sports dataset

## 5 Conclusions

Recognizing diverse actions in surveillance video data, including human activity recognition (HAR), heavily relies on spatiotemporal features.