## 3 Experimental Results and Analysis
### Comparisons with State-of-the-Art Methods  
In order to verify the performance of the proposed method on the PETA and RAP datasets, we compared it with the two traditional pedestrian attribute recognition representative methods; iKSVM [2,17] and ELF [18], and another four deep learning-based methods, including ACN [5], DeepMAR [19], WPAL [8] and multi-task CNN [6]. The traditional ikSVM method uses the handcrafted features and the SVM classifier framework to recognize the attributes, and a classifier is trained for each attribute. In order to achieve a better recognition performance, the number of positive samples or negative samples is intentionally balanced in an artificial manner. ELF [18] (ensemble of localized features), a method of using local features, combines the features of eight color channels and luma channels to obtain feature representations and then uses AdaBoost as the classifier. In deep learning-based methods, ACN [5] adopts a joint training CNN model that uses only the dependencies among attributes, but not pedestrian gestures, context, etc. Furthermore, a N/A label is added, for the first time in the labeling process, which denotes the uncertainty of certain attributes. The DeepMAR [19] method has often been used as the benchmark. This paper proposes two models. One is for the recognition of a single attribute and the other for multiple attributes. A simple CNN model is proposed for feature extraction and joint training of multiple attributes. The recognition accuracy has been improved by using the correlation of attributes. In contrast to previous methods of attributes recognition using the whole pedestrian image, WPAL [8] first determines the location of different attributes by the weakly supervised method, obtaining the attribute detection result, and then predicts the attributes in the detected results. Among the existing deep learning-based pedestrian attributes recognition methods, the multi-task CNN [6] method can achieve state-of-the-art recognition performance. The basic idea is to classify pedestrian attributes in a multi-task manner. Table 3 shows a comparison of the recognition accuracy of the proposed method and the other five methods on the PETA dataset. As shown in Table 3, compared with the current deep learning-based pedestrian attributes recognition methods, the proposed method can obtain the highest recognition accuracy of 91.88%, which is 3.68% higher than that of multi-task CNN. We also conducted experiments on the RAP dataset. The attribute labels were divided into two groups according to the behavior attribute and the item attribute, and a 92-dimensional label vector was generated. The comparison results of the recognition accuracy on the RAP dataset are shown in Table 4. \begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **mA** \\ \hline iKVM & 82.75\% \\ ELF & 75.21\% \\ ACN & 81.15\% \\ DeepMAR & 82.89\% \\ WPAL & 85.50\% \\ multi-task CNN & 88.20\% \\ Ours & 91.88\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison results using different methods on the PETA dataset. As shown in Table 4, compared with the current deep learning-based pedestrian attributes recognition methods, the proposed method can also obtain the highest recognition accuracy of 87.44% on the RAP dataset, which is 4.21% higher than that of multi-task CNN. The main reasons why the proposed method can achieve superior recognition accuracy are:  
1. The unique design of the BN layer and the dropout layer are adopted, which results in the improvement of recognition accuracy of the attributes. 2. The integration of the attributes labels to form a label vector is equivalent to re-assigning a new label to each pedestrian image. By using CNN, the mapping relationship between the pedestrian image and the label vector can be established with high efficiency. The label vector combines all the attributes together, and thus, can fully exploit the intrinsic correlation among the attributes, improving the recognition accuracy. 3.