# Deep Learning-Based Human Action Recognition with Key-Frames Sampling Using Ranking Methods
## 4 Experimental Results
### Performance Evaluations and Comparisons  
We carefully analyze the dataset and choose \(k\) as 16, 20, and 24, with a difference of 4 that provides better results. We evaluate the performance of the proposed method on the DHA, MSR-Action3D, and UTD-MHAD human action datasets. Table 1 lists the recognition performance for the CCM and SSIM metrics with 16, 20, and 24-ranked frames. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multirow{2}{*}{**Ranking**} & **DHA** & **MSR-Action3D** & **UTD-MHAD** & **Average** \\ \hline \multirow{4}{*}{CCM (\(\rho\))} & \(k=16\) & 91.7\% & 92.4\% & 93.3\% & **92.5\%** \\  & \(k=20\) & 92.6\% & 93.1\% & 93.5\% & **93.1\%** \\  & \(k=24\) & 92.2\% & 93.8\% & 93.5\% & **93.2\%** \\ \hline \multirow{4}{*}{SSIM (\(\psi\))} & **Average** & **92.2\%** & **93.1\%** & **93.4\%** & **92.9\%** \\  & \(k=16\) & 92.2\% & 93.5\% & 94.2\% & **93.3\%** \\ \cline{1-1}  & \(k=20\) & 93.0\% & 94.2\% & 94.4\% & **93.9\%** \\ \cline{1-1}  & \(k=24\) & 93.5\% & 94.6\% & 94.7\% & **94.2\%** \\ \hline \multirow{4}{*}{**Average**} & **Average** & **92.9\%** & **94.1\%** & **94.4\%** & **93.8\%** \\ \cline{1-1}  &The average recognition results using the CCM metric are approximately 92.2% for the DHA, 93.1% for the MSR-Action3D, and 93.4% for the UTD-MHAD datasets, respectively. For the SSIM metric, the proposed method achieves approximately 92.9% for the DHA, 94.1% for the MSR-Action3D, and 94.4% for the UTD-MHAD datasets. If the value of \(k\) is increased from 16 to 20, the average performance improvement is about 0.6% for the CCM and SSIM metrics. On the other hand, if the value of \(k\) is increased from 20 to 24, the average performance improvement is about 0.1% for the CCM and 0.3% for the SSIM metrics, which are comparatively much lower. This is because the temporal information remains almost the same even when we increase the number of sampling frames along the temporal direction, such as 16, 20, and 24. However, if we compare the effectiveness of sampling metrics, the SSIM metric can sample more effectively, achieving an average accuracy of 93.8%, than the CCM metric with an average accuracy of 92.9%. To show the effectiveness of the proposed system on different datasets, we compare the recognition results with several state-of-the-art methods, as provided in Table 2. The first, second, and third columns represent the recognition results of the prior works on the DHA, MSR-Action3D, and UTD-MHAD datasets, respectively. The proposed method can improve by approximately 10%, 5%, and 9% greater average accuracy than the prior works on the DHA, MSR-Action3D, and UTD-MHAD datasets, correspondingly. This is because most of the prior methods encoded the entire sequence into the dynamic images and used 2D-CNN or traditional machine learning techniques to classify the human action. While encoding an action into the spatial format, it cannot capture full temporal changes that reduce the overall performance. Even though few methods conducted 3D-CNN and LSTM, they did not provide better results because of the selection of frames from a whole sequence for training and testing. The network configurations also have a great effect on the performance of the human action recognition systems. The proposed method ensures better performance on the three different datasets because the proposed ranking metric can effectively select \(k\)-ranked frames that contain meaningful temporal information. The 3D-CNN can extract the discriminative features from the selected frames and provides better results. From the above results, it can be stated that the proposed method with the SSIM metric having \(k=24\) provides the best or similar results. We show the confusion chart for SSIM