## 5 Results and Discussion
### Action Recognition Performance  
The performance of the proposed methodology for action recognition on the UCF Sports dataset was evaluated using the leave-one-out (LOO) cross validation scheme (i.e., for each action class), and the DDNet was trained on all the videos for that action class except one, which was left out for testing. This process was repeated in a cyclical manner for every video of each action class in the dataset (i.e., for a particular action class, each video for that action class was used one by one for testing, whereas the remaining videos were used for training the DDNet). For each cycle, the weights of the DDNet were initialized (i.e., the weights from the previous cycle were not considered). The overall recognition accuracy for a particular action class was determined by calculating the average accuracy for all the videos. The average accuracy over all the iterations of this process for each action class was then used to measure and compare the action recognition performance of different methods. The confusion matrix of the proposed methodology for the UCF Sports dataset is given in Figure 8, which shows the performance of the proposed methodology for the _10_ different action categories in the UCF Sports dataset. In Figure 8, these action categories  
Figure 7: An example of the human keypoints extracted by the proposed method from a key frame of one the videos in the self-collected datasets. The proposed framework simultaneously performs action, face and emotion recognition on the selected key frame in real time. The human objects in this image have been labeled with their estimated distances, identified actions, emotions and identities through face recognition. For instance, from left to right, the identified actions and emotions for each human object are (waving hand, happy), (sitting, neutral), (standing, neutral), and (standing, neutral). are represented by labels from 0 to 9. Table 1 provides a description or specification of these labels (i.e., the action category that each of these labels corresponded to along with the number of video clips in the dataset for each action category). The blue color bar to the right of the confusion matrix in Figure 8 represents the number of video clips corresponding to each action category, which is then highlighted in the confusion matrix using the appropriate color from this blue color bar corresponding to the number of video clips for each action category. We can observe in Figure 8 that for 7 out of 10 action classes, the proposed methodology achieved 100% classification accuracy. However, for three action categories--_horse-riding_, _swing-side_ and _swing-golf_--its classification performance deteriorated. This was perhaps the result of similarity between the spatiotemporal changes for these action categories. In Table 2, we provide a comparison of the action recognition performance of the proposed method and several state-of-the-art methods [33; 34; 35; 36; 37; 38] on the UCF Sports dataset. The proposed method achieved an accuracy of 93.1%, which was better than all the state-of-the-art methods, except for the one proposed in [38] that uses 3D convolutional neural network (CNN)-based fused feature maps and a long short-term memory (LSTM) recurrent neural network (RNN). The method proposed in [38] achieved only a marginal performance gain of 0.8% but at a much higher computational cost. The proposed key frame-based skeleton feature estimation and action recognition network (KFSENet) had comparatively lower computational complexity but achieved action recognition performance that was better than most of the existing methods. Furthermore, our results also indicate the advantages \begin{table}
\begin{tabular}{c c c} \hline \hline
**Label** & **Specification** & **No. of Video Clips** \\ \hline
0 & Diving & 14 \\
1 & Kicking & 20 \\
2 & Lifting & 6 \\
3 & Horse-riding & 12 \\
4 & Running & 13 \\
5 & Swing-side & 13 \\
6 & Stateboarding & 20 \\
7 & Swing-bench & 12 \\
8 & Walking & 22 \\
9 & Swing-golf & 18 \\ \hline \hline \end{tabular}
\end{table}