###### Abstract  
Pedestrian attributes (such as gender, age, hairstyle, and clothing) can effectively represent the appearance of pedestrians. These are high-level semantic features that are robust to illumination, deformation, etc. Therefore, they can be widely used in person re-identification, video structuring analysis and other applications. In this paper, a pedestrian attributes recognition method for surveillance scenarios using a multi-task lightweight convolutional neural network is proposed. Firstly, the labels of the attributes for each pedestrian image are integrated into a label vector. Then, a multi-task lightweight Convolutional Neural Network (CNN) is designed, which consists of five convolutional layers, three pooling layers and two fully connected layers to extract the deep features of pedestrian images. Considering that the data distribution of the datasets is unbalanced, the loss function is improved based on the sigmoid cross-entropy, and the scale factor is added to balance the amount of various attributes data. Through training the network, the mapping relationship model between the deep features of pedestrian images and the integration label vector of their attributes is established, which can be used to predict each attribute of the pedestrian. The experiments were conducted on two public pedestrian attributes datasets in surveillance scenarios, namely PETA and RAP. The results show that, compared with the state-of-the-art pedestrian attributes recognition methods, the proposed method can achieve a superior accuracy by 91.88% on PETA and 87.44% on RAP respectively. p edestrian attributes recognition; lightweight convolutional neural network; multi-task; surveillance scenarios +
Footnote †: journal: Medical  
1. The pedestrian images in the acquisition process are affected by many complex factors, for example, illumination, low image quality, multi-view, blur, and occlusion, which has a serious impact on pedestrian attributes recognition. 2. Pedestrians' belongings, such as backpacks and luggage, also bring certain difficulties to pedestrian attributes recognition. 3. Each pedestrian possesses different attributes, therefore, the data distribution of each attribute in the datasets is not balanced, such as wearing a hat, black clothing and purple clothing [1]. In essence, pedestrian attributes recognition is a multi-label classification problem, because pedestrian attributes are related to each other and are not completely exclusive. Many studies have been carried out on the problem of multi-label recognition of pedestrian attributes. On the whole, pedestrian attributes recognition generally adopts the framework of \"feature extraction + classifier\". According to the features extracted for attributes recognition, the development of pedestrian attributes recognition can be divided into two stages:  
The first stage: Before 2012, handcrafted features were used to train the classifiers, such as Support Vector Machine (SVM), to obtain the pedestrian attributes recognition model. The handcrafted features used included global features and local features. The commonly used global features included low-level visual features such as color and texture, which are easily affected by some factors such as scene environment, occlusion, and illumination. The local features focus on the detailed information of the image and have more advantages in the representation of the pedestrian attributes than the global features. [PERSON] et al. [2] labeled 21 kinds of pedestrian attributes, including clothing style, gender, and hairstyle, and respectively trained the classifier for each attribute. In the training process, pedestrian images from different angles of the camera were selected to improve the robustness to angle changes. [PERSON] et al. [3] established the APIS database and labeled 13 kinds of attributes, mainly for pedestrian attributes recognition in complex scenes.