## 4 Experiments
### Dataset and Training  
**KTH**. The KTH dataset contains six types of human actions with 2391 video sequences. All sequences were taken over homogeneous backgrounds with a static camera with a 25-fps frame rate. These sequences were down-sampled to a spatial resolution of 160 \(\times\) 120 pixels and have a length of 4 s on average.  
**UCF101**. The UCF101 dataset includes a total number of 101 action categories with 13,320 videos, which have divided into five types. The clips of one action class are divided into 25 groups which contain four to seven clips each. All clips have a fixed frame rate and resolution of 25 fps and 320 \(\times\) 240, respectively.  
Figure 5: The connection structure of composite function in a dense block.  
In our experiments, all the dataset subjects were divided into a training set, a validation set, and a test set. The classifiers were trained on a training set while the validation set was used to optimize the parameters of each method. The presented recognition results were obtained on the test set. In the KTH dataset, all sequences were divided with respect to the subjects into a training set (eight persons), a validation set (eight persons), and a test set (nine persons). In the UCF101 dataset, we used the three splits into training and test data, and the performance was measured by the mean classification accuracy across the splits.  
All the networks were trained using stochastic gradient descent (SGD) [19]. The network initial learning rate was set to 0.1, which decreased to 0.01 at 1/2 epochs and 0.001 at 3/4 epochs. In our model, we used a weight decay of \(10^{-4}\) and a Nesterov momentum [20] of 0.9 without dampening for all the weights. The network growth rate was set to 12 or 24. The batch size was set to 4, 8, or 10 according to the growth rate and the depth of the network; the higher the growth rate and depth, the lower the batch size. In the KTH dataset, the batch size was set to 4 or 8. We trained this model for 60 epochs; we divided the learning rate by 10 at epoch 30 and divided it by 100 at epoch 45. In the UCF101 dataset, the batch size was set to 8 or 10. We trained this model for 100 epochs; we divided the learning rate by 10 at epoch 50 and divided it by 100 at epoch 75. Due to GPU memory constraints, the maximum crop size of the video (which is the height and width of the image) was set to 256 \(\times\) 256. In the spatial pyramid pooling layer, we used a three-layer pyramid to pool the features on the feature maps. The pyramid was \(\{3\times 3,2\times 2,1\times 1\}\). When the pyramid pooling layer received the feature maps from Dense Block3, the size and stride of the filters were calculated through Equation (1).  
Video data includes three-dimensional information, including depth, height, and width. Therefore, when using spatial pyramid pooling, we need to consider ignoring depth information and make sure that the depth information is the same at each level of the SPP. We only need to pool on the two dimensions of height and width. Of course, we can also consider pooling directly in three dimensions. However, if this is undertaken, we need to perform spatial pyramid pooling on the spatial dimension and temporal pyramid pooling on the time dimension. This will be studied in our further research work.