# Human Activity Classification Using the 3 DCNN Architecture
## 4 Conclusions and Discussion  
In this paper, we designed a neural network capable of classifying human activities from video using a 3 DCNN architecture. We used the UCF YouTube Action database for training and testing. The classification was carried out for 11 classes: Basketball, Biking, Diving, GolfSwing, HorseRiding, SoccerJuggling, Swing, TennisSwing, TrampolineJumping, VolleyballSpiking, and WalkingDog. Videos in the dataset were cropped to an input \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Train/** & **UCF YouTube** & **Modified UCF101** & **Full UCF50** & **Full UCF101** \\
**Test Parameters** & **Action Dataset** & **Dataset** & **Dataset** & **Dataset** \\ \hline Train loss & 0.08 & 0.07 & 0.14 & 0.21 \\ Train accuracy & 97.6\% & 98.5\% & 91.2\% & 87.8\% \\ Test loss & 1.51 & 1.43 & 1.61 & 1.79 \\ Test accuracy & 85.2\% & 84.4\% & 82.2\% & 79.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy and loss function model during training and testing after 35 epochs. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluation Metrics** & **UCF YouTube** & **Modified UCF101** & **Full UCF50** & **Full UCF101** \\
**Dataset** & **Action Dataset** & **Dataset** & **Dataset** \\ \hline Precision (P) & 87.4\% & 82.7\% & 80.6\% & 78.5\% \\ Recall (R) & 85.6\% & 87.7\% & 84.8\% & 81.2\% \\ F1 score (F1) & 86.5\% & 85.1\% & 82.6\% & 80.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The evaluation criterion of the proposed neural network architectures using different datasets. \begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm for Recognition** & **Accuracy [\%]** \\ \hline Architecture for motion features [7] & 65.4 \\ Architecture for static features [7] & 63.1 \\ Architecture for hybrid features [7] & 71.2 \\ Proposed 3 DCNN architecture previous research [22] & 29 \\ Proposed architecture & 85.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of accuracy obtain by different neural network architectures using UCF YouTube Action dataset. size of 32 \(\times\) 32 \(\times\) 3 RGB. This choice could influence the overall accuracy obtained. However, we were able to evaluate whether the 3 DCNN architecture was able to classify the videos with minimal errors. This statement is also confirmed by Figures 9 and 10, where we can observe an increase in accuracy and a decrease in the loss function. Based on the obtained results, we can state that such a neural network is able to classify video data containing a variety of human activities. During training, the loss function decreased to 0.08 and the accuracy increased to 97.6%. We can evaluate that the 3 DCNN architecture coped well with the problem of time continuity between frames as it was able to process time information in addition to spatial information. When evaluating the confusion matrix, we can state that the process of classification into classes was successful and had a minimal occurrence of errors. The neural network made the most errors when classifying videos from the HorseRiding category. Videos from this category were most often categorized as WalkingDog. For the overall results using UCF YouTube Action dataset, the evaluation metrics (average test accuracy, precision, recall and F1 score) were obtained. The overall test accuracy was 85.2%. The precision reached value 87.4% and recall 85.6%. The F1 score was 86.5%. The results clearly confirm that we have created a capable neural network architecture for the classification of human activities. In addition to spatial information, the neural network can also process time information. However, we want to continue our work and improve our results. The more accurately the neural network can classify human activity, the more we can use it in real life applications such as personal safety, care for the elderly and protection of minors. For a more complex comparison of the proposed neural network (3 DCNN), the modified UCF101 dataset has been added. The full UCF50 dataset and full UCF101 dataset have also been added.