# Cascading Pose Features with CNN-LSTM for Multiview Human Action Recognition
## 5 Result and Discussion  
This section describes the results achieved by the proposed system. Skeleton detection from 2D image is shown in Figure 3. There are 25 skeleton joints location that are detected by OpenPose [35] and form a 25 \(\times\) 3 matrix, contains X,Y coordinates and confidence map. As the proposed system only utilises the X,Y coordinates for the action recognition purpose, the input to the proposed system becomes a 25 \(\times\) 2 matrix. As mentioned in methodology, this input matrix is given to proposed CNN-LSTM architecture, so that CNN can analyse the skeletal joints location and pass it to LSTM network which classify the performed action. \begin{table}
\begin{tabular}{c c} \hline \hline
**Class** & **Action** \\ \hline
1 & check-watch \\ \hline
2 & Cross-arms \\ \hline
3 & Get-up \\ \hline
4 & kick \\ \hline
5 & Pick-up \\ \hline
6 & Point \\ \hline
7 & Punch \\ \hline
8 & Scratch-head \\ \hline
9 & Sit-Down \\ \hline
10 & Turn-Around \\ \hline
11 & Walk \\ \hline
12 & Wave \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of actions from IXMAS Dataset. Figure 3: Skeleton Detection. Figure 4 shows the training and validation accuracy of the proposed system. Training accuracy started from 93.3% on first epoch where as the validation accuracy started from 93.2%. As the model training process continues, after 10 epochs training and validation accuracy becomes same around 94.4%. The model was trained on 20 number of epochs, but after 10 epochs training and validation accuracy was constant. Figure 5 shows the loss during training and validation, as the number of epochs increases model loss was decreasing and after 10 number of epochs model loss becomes constant around 0.025. Figure 6 shows the confusion chart of the proposed system. More statistical results such as Precision, Recall and F1-Score are also added to show the efficacy of the proposed in Table 4. Statistical results also show the performance of model in term of precision, recall and F1-Score, and the proposed model is working well for each class. The proposed model takes 434 s in total during training and approximately 22 s for each epoch. Table 5 shows the comparative analysis of proposed method with the state of the art work using MCAD dataset. Comparatively our model improved 37.6% accuracy from that achieved previously by Cuboid features [36], 30.1% from the Covariance matrices [37], 12.7% from the STIP features [36], 10.2% from the IDT [40] and 7.5% from the Conflux LSTM network [38]. These results confirmed the superior performance of our method as shown in Table 5.  
Figure 4: Accuracy of the proposed model during training and testing using MCAD Dataset. Figure 5: Loss of the proposed model during training and testing using MCAD Dataset. For further validation with the state-of-the-art we executed our proposed method on IXMAS dataset using overall accuracy. Figure 7 shows the accuracy during training and testing, whereas Figure 8 shows loss during training and testing. Figure 9 shows the confusion chart of the proposed system using IXMAS dataset. Statistical results such as Precision, Recall and F1-Score are also added to show the efficacy of the proposed in Table 6. Comparatively as shown in Table 7 our model improved 1.92% accuracy from that achieved previously by Shape Features [41], 11.12% from the LBP [42], 8.64% from