# Best Frame Selection to Enhance Training Step Efficiency in Video-Based Human Action Recognition
## 4 Result and Discussion
### Implementation Details
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{4}{c}{**HMDB51**} \\ \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Pre-Processing Type**} & \multirow{2}{*}{**None**} & \multicolumn{4}{c}{**Gradient Types of Keyframes Selection**} \\ \cline{3-8}  & & & & **[PERSON]** & **Perruit** & **Roberts** & **Central D** & **Intermed D** \\ \hline \multirow{2}{*}{Two-Stream I3D [30]} & Train & 72.1 & 76.1 & 71.5 & 72.2 & 70.2 & 72.9 \\ \cline{2-8}  & Test & 65.2 & 68.5 & 68.1 & 67.5 & 68.4 & 68.6 \\ \hline \multirow{2}{*}{Motion Guided Network [31]} & Train & 72.3 & 77.4 & 78.1 & 75.9 & 68.8 & 70 \\ \cline{2-8}  & Test & 68 & 70.3 & 70 & 69 & 68.1 & 68.4 \\ \hline \multirow{2}{*}{Spatiotemporal network [32]} & Train & 67.3 & 69.8 & 69.1 & 70.2 & 73.4 & 70 \\ \cline{2-8}  & Test & 66.4 & 67.4 & 67.4 & 66.6 & 66.3 & 66.1 \\ \hline \multirow{2}{*}{Correlation net [33]} & Train & 73 & 74.7 & 75.9 & 76.7 & 77.8 & 73.1 \\ \cline{2-8}  & Test & 68.1 & 70.7 & 70.1 & 68.4 & 69.1 & 68.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Accuracy of the Train and Test data in the HMDB51 dataset versus Gradient types (D—difference). \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{4}{c}{**UCF101**} \\ \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Pre-Processing Type**} & \multirow{2}{*}{**None**} & \multicolumn{4}{c}{**Gradient Types of Keyframes Selection**} \\ \cline{3-8}  & & & **Spobel** & **Perruit** & **Roberts** & **Central D** & **Intermed D** \\ \hline \multirow{2}{*}{Two-Stream I3D [30]} & Train & 93.5 & 95.6 & 95.7 & 94.3 & 95.6 & 94.3 \\ \cline{2-8}  & Test & 92.5 & 93.1 & 93.1 & 92.9 & 92.9 & 92.9 \\ \hline \multirow{2}{*}{Motion Guided Network [31]} & Train & 96.4 & 97.2 & 95.2 & 96.9 & 96.7 & 96.7 \\ \cline{2-8}  & Test & 94.1 & 95.1 & 94.1 & 94.5 & 94.1 & 93.9 \\ \hline \multirow{2}{*}{Spatiotemporal network [32]} & Train & 94.7 & 97.4 & 97.4 & 95.9 & 96 & 94.7 \\ \cline{2-8}  & Test & 93.8 & 94.7 & 94.7 & 94.0 & 94.2 & 94 \\ \hline \multirow{2}{*}{Correlation net [33]} & Train & 96 & 98 & 95.3 & 95 & 94.7 & 95.5 \\ \cline{2-8}  & Test & 92.8 & 95.2 & 93.9 & 93.8 & 93.6 & 93.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy of the Train and Test data in the UCF101 dataset versus Gradient types (D—difference). The best improvement occurred in the [31] method, where a 7.05% improvement in the training set of the HMDB51 database was achieved. In the UCF101 dataset, the best improvement in the training sets was related to the spatiotemporal network [32], where the system accuracy was improved by 2.85%. According to the results, the improvement in the HMDB51 dataset was more remarkable. Based on the results of Table 6, using the proposed method for pre-processing at the beginning of the HAR systems led, in most cases, to a reduction in the training time, which was due to the elimination of irrelevant inputs in the network training process that sped up the convergence of the training process.