# A Framework for Pedestrian Attribute Recognition Using Deep Learning
## 4 Experiments
### Results and Discussion
The feature extraction process of a CNN can be compared to a black box [50]. From Figure 18, we can deduct that the performance of our proposed framework is also related to the black box concept of a CNN architecture. Our proposed framework performs better in terms of accuracy. Tables 7 and 8 show a comparison among our proposed framework and state-of-the-art(SOTA) methods on the RAP v2 dataset and the PARSE100K dataset, respectively. Our proposed framework outperforms existing SOTA methods with 93.41% accuracy on the RAP v2 dataset and with 89.24% accuracy on the PARSE100K dataset. However, the precision, recall, and f1 score is not very high. As both datasets, i.e., RAP v2 and PARSE100K, contain a class imbalance problem, some pedestrian attributes have only a limited number of positive samples, and some attributes have a limited number of negative samples. Thus, misclassification in a class containing a limited number of positive samples can cause a major reduction in precision and recall values, which also affects the f1 score. Additionally, the different splits can have an impact on the outcome because a split can make the distribution of positive samples limited. The experiments conducted on our proposed framework is affected by the class imbalance problems described above, and hence the precision, recall and f1 score are not very high. In our proposed framework, we applied hard sharing of features. The overall extracted features are considered for the decision-making. The interdependency among the features of different pedestrian attributes is considered in our proposed framework. This factor is one of the reasons for the satisfactory performance. Our proposed framework has shown relatively small value in other evaluation metrics. The selected train, test, and validation data are different in different experiments; our selected split data are also different. Hence, the distribution of the samples is also imbalanced. After applying the data balancing techniques, the results did not improve. As a result, our proposed framework performs well in the majority of class samples. Figure 19 shows the demonstration of our proposed framework. The source of the image is available in [25]. Figure 15: ROC curve of Model 5 on the PARSE100K dataset. Figure 16: Confusion matrix of Model 5 on the RAP v2 dataset. Figure 17: Confusion matrix of Model 5 on the PARSE100K dataset. _Appl. Sci._**2022**, _12_, 622 \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**RAP V2 Dataset**} \\ \cline{2-5}  & **mA** & **mP** & **mR** & **F1** \\ \hline
[25] & 68.92 & 70.89 & 80.90 & 75.56 \\
[1] & 75.54 & 76.56 & 78.64 & 77.59 \\
[48] & 77.87 & 79.03 & 79.79 & 79.04 \\
[49] & 76.74 & 80.42 & 78.78 & 79.24 \\ Ours & **93.41** & 61.34 & 39.15 & 45.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison with the state-of-the-art methods on the RAP v2 dataset. Figure 18: Feature map of each block of Model 5. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**PARSE100K Dataset**} \\ \cline{2-5}  & **mA** & **mP** & **mR** & **F1** \\ \hline
[1] & 72.70 & 82.24 & 80.42 & 81.32 \\
[47] & 74.21 & 82.97 & 82.09 & 82.53 \\
[3] & 81.63 & 84.27 & 89.02 & 86.58 \\
[33] & 77.20 & 88.46 & 84.86 & 86.62 \\ Ours & **89.24** & 58.98 & 40.02 & 42.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison with the state-of-the-art methods on the PARS100K dataset.