# Human Activity Classification Using the 3 DCNN Architecture
## 3 Experimental Results
### Results  
The dataset was divided into three parts: A training set, a testing set and a validation set (70:20:10). The training set serves to train the neural network. The training set contains 812 videos, which represents 70% of the datasets. The test set to objectively evaluate the fit model was used. The testing set contains 232 videos, which represents 20% of the datasets. The validation set for objective evaluation of the model and for tuning of hyperparameters was used. The validation set contains 116 videos, which represents 10% of the datasets. The ratio of testing, training and validation videos has been set to exact. A testing videos were not taken into account when a training the data. In this paper, we classify the dataset (videos) into the following 11 classes (Basketball, Biking, Diving, GolfSwing, HorseRiding, SoccerJuggling, Swing, TennisSwing, TrampolineJumping, VolleyballSpiking, and WalkingDog). The data must be pre-processed before being input to the neural network. Firstly,  
Figure 7: Sample images from the UCF YouTube Action Dataset [5,6]. the each video is cut into 320 \(\times\) 240 frames. Each frame is saved to a field containing other frames from the given category. The partial accuracy during training process increases with the minimization of the loss function. A positive increase in accuracy with each new epoch during training can be seen in Figure 9. In the early epochs, we see a rapid increase in accuracy. At a value of approximately 0.8 the change in accuracy slows but continues to increase. The highest accuracy reached during training is 95%. In the Figure 10 is shown the decrease of the loss function during training process. The decrease of the loss function is directly proportional to the accuracy, which indicates good classification results. In the first epochs of training, the rapid drop in values is observed. Around the value of 0.5, the change in the loss function slows, but continues to decrease to its lowest value of approximately 0.08. The decrease in the loss function indicates error minimization, which is reflected by a directly proportional increase in the accuracy. Most of the time we would observe that accuracy increases with the decrease in loss (but this is not always the case). These two parameters (accuracy and loss function) have different definitions and measure different things. They often appear to be inversely proportional but there is no mathematical relationship between these two metrics. Next, the model that generates a confusion matrix was created as is shown in Table 1. In this matrix, we see how the classification of individual classes proceeded. The classes are divided into the following categories (Table 1): 1. Basketball, 2. Biking, 3. Diving, 4. GolfSwing, 5. HorseRiding, 6. SoccerJuggling, 7. Swing, 8. TennisSwing, 9. TrampolineJumping, 10. VolleyballSpiking, 11. WalkingDog. The confusion matrix shows that the neural network had the most difficulty processing videos from the HorseRiding category. Incorporation  
Figure 8: Sample images from the UCF101 Dataset [28]. classifications include four videos as WalkingDog, one video as swing, 1 asTennisSwing, two as biking and 2 as basketball. These results may indicate that the selected videos reminded the network of the surrounding environment, the presence of an animal or the angle of the camera. For example, the error of classifying a video from HorseRiding to Walkingdog could be due to the high similarity of the environment and the occurrence of an animal. In addition to the similarity in the videos, the classification results may have been influenced by factors such as video quality or the size of the input frames, which we input as 32 \(\times\) 32 \(\times\) 3 RGB (red green blue). Different sizes of input images could also cause different results. We chose the size of the input frames specifically based on the optimization of the memory requirements of the system with respect to the functionality of the algorithm. Figure 10: Loss function during training process.