# Pedestrian Attribute Recognition with Graph Convolutional Network in Surveillance Scenarios
## 1 Introduction  
Video surveillance is a part of our daily life, with the advent of the era of artificial intelligence, intelligent video analytics attaches great importance to the modern city since it can pre-alarm abnormal behaviors or events [1]. In this paper, we mainly focus on the pedestrian in the surveillance system. Human attribute analysis has recently drawn a remarkable amount of attentions by researchers for person detection and re-identification, as well as widely applied in plenty of aspects [2]; besides, pedestrian structured representation can obviously reduce surveillance video storage and improve pedestrian retrieve speed in the surveillance system. However, there are still plenty of challenges. For one thing, human attributes naturally involve largely imbalanced data distribution. For example, when collecting the attribute \"Bald\", most of them will be labeled as \"No Bald\" and its imbalanced ratio to the \"Bald\" class is usually very large [3]. For another, there are plenty of uncertainties in practical scenarios, the resolution of images may be lower and the human body may be blocked by other things [4]. Furthermore, the collecting of labeled samples is labor-consuming. Extreme learning machine (ELM) has gained increasing interest from various research fields for certain years. Apart from classification and regression, ELM has recently been extended for clustering, feature selection, representational learning, and many other learning tasks [5]. As an efficient single-hidden-layer feed forward neural network, which has generally good performance and fast learning speed, ELM has been applied in a variety of domains, such as computer vision, energy disaggregation [6], and speech enhancement [7]. In [8], authors proposed a novel pedestriandetection method using multimodal Histogram of Oriented Gradient for pedestrian feature extraction and extreme learning machine for classification to reduce the detection rate of false positives and accelerate processing speed. The experimental results have proved the efficiency of the ELM based method. Recently, researchers mostly used convolutional neural network to extract image features due to the rapid development of deep learning. As for multi-label classification, researchers have proposed some approaches based on the probabilistic graph model or recurrent attention model to deal with the problem. It is worth mentioning that attention mechanisms is also a popular method. Inspired by [9], we propose a novel model based on the graph convolutional network to model the correlations between labels. For example, when the label \"Long Hair\" occurs, the label \"Female\" is much more likely to show up than the label \"Male\". Following this idea, we construct an adjacency matrix between labels to deliver this correlation into classifiers, then combine the image representation to produce multi-label loss. Our code is hosted at [[https://github.com/2014](https://github.com/2014) gaokao/pedestrian-attribute-recognition-with-GCN]([https://github.com/2014](https://github.com/2014) gaokao/pedestrian-attribute-recognition-with-GCN). The contributions of the paper are as follows:  
* This paper creatively applies the novel end-to-end trainable multi-label image recognition framework to pedestrian attribute recognition, which to our knowledge, is the first one tackling pedestrian attribute recognition by graph convolutional network. * Graph convolutional network normally propagates information between nodes based on correlation matrix. So, we design the correlation matrix for GCN in-depth and propose some improvement methods. Finally, we evaluate our method on pedestrian attribute recognition dataset, and our proposed method consistently achieves superior performance over previous competing approaches. The result of the paper is organized as follows: Section 2 comprehensively introduces related work in pedestrian attribute recognition. Section 3 describes the overall architecture of our model in detail. Section 4 verified the superiority of our method using experiments.