Pedestrian Attribute Recognition with Graph Convolutional Network in Surveillance Scenarios

Xiangpeng Song, Hongbin Yang and Congcong Zhou
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Xiangpeng Song: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Hongbin Yang: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Congcong Zhou: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China

Future Internet, 2019, vol. 11, issue 11, 1-13

Abstract: Pedestrian attribute recognition is to predict a set of attribute labels of the pedestrian from surveillance scenarios, which is a very challenging task for computer vision due to poor image quality, continual appearance variations, as well as diverse spatial distribution of imbalanced attributes. It is desirable to model the label dependencies between different attributes to improve the recognition performance as each pedestrian normally possesses many attributes. In this paper, we treat pedestrian attribute recognition as multi-label classification and propose a novel model based on the graph convolutional network (GCN). The model is mainly divided into two parts, we first use convolutional neural network (CNN) to extract pedestrian feature, which is a normal operation processing image in deep learning, then we transfer attribute labels to word embedding and construct a correlation matrix between labels to help GCN propagate information between nodes. This paper applies the object classifiers learned by GCN to the image representation extracted by CNN to enable the model to have the ability to be end-to-end trainable. Experiments on pedestrian attribute recognition dataset show that the approach obviously outperforms other existing state-of-the-art methods.

Keywords: pedestrian attribute recognition; graph convolutional network; multi-label learning (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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