## 4 Experiments
### Performance  
In our experiments, we trained our model with different depths and growth rates. To compare with our model, we carried out a number of experiments separately through the use of spatial pyramid pooling layer and without spatial pyramid pooling (3D-DenseNet-SPP and 3D-DenseNet). The configuration of our model parameters and main results achieved with the KTH and UCF101 datasets are shown in Table 1. The pyramid is \(\{3\times 3,2\times 2,1\times 1\}\) in the 3D-DenseNet-SPP structure described in Table 1.  
From Table 1, we can see that 3D-DenseNet-SPP was generally more accurate than 3D-DenseNet. The maximum accuracy of 3D-DenseNet-SPP was 0.9197, and the maximum accuracy of 3D-DenseNet \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Depths** & **Growth Rate** & **Batch Size** & **Params** & **KTH** & **UCF101** \\ \hline
3D-DenseNet & 30 & 24 & 4 & 0.9 M & 0.8990 & - \\
3D-DenseNet & 30 & 12 & 8 & 0.6 M & 0.8698 & 0.8398 \\
3D-DenseNet & 20 & 12 & 8 & 0.4 M & 0.8894 & 0.8653 \\
3D-DenseNet & 20 & 24 & 8 & 0.6 M & 0.9110 & 0.8738 \\
3D-DenseNet & 20 & 24 & 10 & 0.6 M & - & 0.8723 \\
3D-DenseNet-SPP & 20 & 12 & 8 & 0.1 M & 0.8924 & 0.8710 \\
3D-DenseNet-SPP & 20 & 24 & 8 & 0.4 M & **0.9197** & 0.8796 \\
3D-DenseNet-SPP & 20 & 24 & 10 & 0.4 M & - & **0.8894** \\
3D-DenseNet-SPP & 30 & 24 & 4 & 0.6 M & 0.9155 & - \\
3D-DenseNet-SPP & 30 & 12 & 8 & 0.5 M & 0.8976 & 0.8829 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Accuracy of the KTH and UCF101 datasets. was 0.9110 in our experiments. As a result, the accuracy was improved by 0.87% in the KTH dataset. The recognition performance averaged across five random trials is reported in Table 2, along with published results in the literature. We used the same training and test splits of the KTH dataset, except 3 DCNN, in Table 2. In the UCF101 dataset, we also compared two sets of experimental data. The maximum accuracy of 3D-DenseNet was 0.8738, while the maximum accuracy of 3D-DenseNet-SPP was 0.8894. The results showed that our accuracy increased by 1.56%. Therefore, the experiments showed that our model improved on KTH dataset and UCF101 dataset compared to the general 3D densely connected network. This shows that our improvement is effective. Our model not only has considerable experimental accuracy, but also has better parameter efficiency. With the same configuration of 3D-DenseNet-SPP and 3D-DenseNet, it is clear that the parameters of 3D-DenseNet-SPP have much lower values than those of 3D-DenseNet. For example, when the depth, growth rate, and batch size are 20, 24, and 8, respectively, 3D-DenseNet-SPP has a parameter of 0.4 M and 3D-DenseNe has a parameter of 0.6 M. All of these are shown in Table 1. This observation shows that our model can increase parameter efficiency and reach a higher accuracy with the  
In order to evaluate our model better, we also draw the precision-recall (PR) curves. The PR curves of our approach on the KTH dataset and the UCF101 dataset are shown in Figure 8a,b, respectively. Obviously, our model can already achieve a better performance. Furthermore, we made some comparisons with existing models concerning accuracy. The results can be found in Tables 3 and 4. The results from Tables 3 and 4 show that our model has a fairly good performance. In fact, our experiments only use RGB images as input, without any data enhancement or optimization. Thus, our method has the potential to reach a higher accuracy via data enhancement measures and testing different configurations of the network. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **KTH** \\ \hline [PERSON] et al. [17] & 71.7\% \\ \hline [PERSON] et al. [22] & 90.00\% \\ \hline [PERSON] et al. [23] & 87.78\% \\ \hline [PERSON] et al. [21] & 81.20\% \\ \hline Ours & **91.97\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A comparison between the proposed method and existing models for the KTH dataset. Figure 8: (**a**) Precision-recall (PR) curves of 3D-DenseNet-SPP and 3D-DenseNet on the KTH dataset.