# Human Activity Classification Using the 3 DCNN Architecture
## 3 Experimental Results
### Results
Overall, the test accuracy of 74.2% using UCF YouTube Action dataset was obtained. Accuracy is the percentage of correct answers relative to the total number. In addition to the average accuracy, we also calculated the precision, recall and F1 score. Precision corresponds to the probability of the detected instances of the activity to its actual occurrence, and in our case, the precision is 77.4%. Recall is 75.6%, and the F1 score is 76.5%. Recall describes the ability to not identify a positive example as negative. The F1 score is defined as a measure that provides a balance between recall and precision. The accuracy result obtained using the proposed 3 DCNN network is compared with accuracy results previously reported for other networks (different architecture for motion, static and hybrid features) [7]. We also compared the results of the proposed architecture with previous research [22]. In previous research, we compared CNN, ConvLSTM and 3 DCNN [27,29]. However, these architectures were tested on a different database, so the steps of our research led to a comparison of our proposed 3 DCNN architecture in this paper with the previous one on the same UCF Youtube Action database. We also applied our proposed architecture on full UCF101 dataset and modified UCF101 dataset. The modified UCF101 dataset consists of 15 classes (1. Baseball Pitch, 2. Basketball Shooting, 3. Bench Press, 4. Biking, 5. Billiards Shot, 6. Breaststroke, 7. Clean and Jerk, 8. Diving, 9. Drumming, 10. Fencing, 11. Golf Swing, 12. High Jump, 13. Horse Race, 14. Horse Riding, 15. Hula Hoop). These classes from the full UCF101 dataset were randomly selected. The full UCF101 dataset is more demanding, because it is expansive. The overall test accuracy of 84.4% using modified UCF101 dataset was achieved by the proposed neural network. On the other hand the overall test accuracy of 79.9% using full UCF101 dataset was achieved. Our results prove that the proposed architecture is applicable to different datasets, which contain human activities. The confusion matrix for modified UCF101 dataset is shown in Table 2.  
The evaluation metrics (P, R, F1) for proposed neural network using UCF YouTube Action dataset, modified UCF101 dataset and full UCF101 dataset were calculated (Table 3). The resulting accuracy was obtained as the sum of correctly predicted samples to the sums of all samples (85.2% using UCF YouTube Action dataset, 84.4% using modified UCF101 dataset, 82.2% using full UCF50 dataset and 79.9% using full UCF101 dataset). In the Table 4, the comparison between accuracy and loss function for 35 epoches (training and testing procedure for the proposed neural network) is shown. In our case, the train loss is the value of the objective function that was minimized. This value (training loss) was calculated over the entire training dataset. On the other hand, the training accuracy means that identical images were used both for training and testing, while test  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**Predicted/Targeted** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** & **14** & **15** \\ \hline
1 & 28 & 0 & 0 & 0 & 0 & 0 & 0 &  
accuracy represents that the trained model identifies independent images that were not used in training. The proposed 3 DCNN architecture achieved the best experimental results compared with other neural network architectures based on motion, static and hybrid features but also on our previous research. All approaches (Table 5) were tested on the same dataset. Our previous architecture was designed and tested on a reduced UCF101 dataset (10 classes), where it achieved results of 72% [22]. However, as soon as we used the UCF Youtube Action dataset, we got results only 29% [22]. This significant decrease in accuracy could have occurred due to overfitting. We subsequently modified the architecture. Based on the observation of the change in accuracy during the addition and removal of layers, we removed two layers.