###### Abstract
The AdaBoost classifier and K-NN (Nearest Neighbors) classifier were respectively adopted to perform the recognition of binary attributes and multiple attributes. [PERSON] et al. [4] constructed a dataset for pedestrian detection, tracking, and re-identification, and also labeled 14 kinds of pedestrian attributes, including clothing, gender, and height. They then calculated the reliable recognition score of each attribute for person re-identification. In conclusion, the pedestrian attributes recognition using handcrafted features has the following deficiencies:  
1. The handcrafted features are less robust to variations in environment, illumination, camera angle, etc. 2. A classifier needs to be designed for the recognition of each attribute, so the complexity of implementation is very high. 3. The intrinsic relationship among attributes is often ignored and not fully utilized. The second stage: After 2012, deep learning has made important breakthroughs in many computer vision tasks such as target detection and tracking, image segmentation, and image classification. Convolutional neural networks (CNN) and other deep neural networks have been widely used to extract the deep features of pedestrians. With the assistance of the powerful and efficient representation of deep features, the recognition accuracy of pedestrian attributes can be improved significantly. A review of the deep learning-based approaches shows that not only the has the design of the network structure been studied, but the problem has also been studied from different aspects. Hence, there are many different research ideas, including global-based, local parts-based, sequential prediction-based, visual attention-based, curriculum learning-based, new designed loss function-based, and graphic model-based ideas. [1]. Below we list a few representative works. For global-based methods, ACN [5] adopts a joint training CNN model and proposes multi-branch classification layers for each attribute. The model uses only the dependencies among the attributes, but not pedestrian gestures, context, etc. Part-based methods often jointly utilize local and global information. For example, [PERSON] et al. [6] proposed a multi-task CNN method. First, according to the spatial location and semantic relationship of the attributes, the attributes are grouped into local and global attributes. Then, the two groups of attributes are classified using different CNN models in a multi-task manner. For sequential prediction-based models, [PERSON] et al. [7] proposed a joint recurrent learning (JRL) model, mining attribute context information and the relationship among attributes to improve the recognition accuracy. In this work, a new Recurrent Neural Network (RNN) coding-decoding network was specifically designed. The context information among pedestrians and the internal attributes of pedestrians were modeled together to learn an integrated network for pedestrian attributes recognition. For loss function-based models, WPAL [8] first determines the location of different attributes by the weakly supervised method, obtains the attribute detection result, and then predicts the attributes in the detected results. For curriculum learning-based methods, [PERSON] et al. [9] proposed the idea of combining the advantages of both multi-task and curriculum learning, introducing curriculum learning into the person attribute recognition task. [PERSON] et al. [10] designed an attribute person recognition (APR) network, which combines the pedestrian ID with labeled pedestrian attributes information, such as gender, hair, and clothing, to train the recognition model. Finally, the trained network model can be used to predict both pedestrian ID information and the pedestrian attributes. This method is also called a join verification network [11], which can improve the generalization ability of the network by combining ID loss and attribute loss. In contrast to the previous attention-based methods, [PERSON] et al.