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HongBin Yang
School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China

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Journal article
Published: 19 November 2019 in Future Internet
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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.

ACS Style

Xiangpeng Song; HongBin Yang; Congcong Zhou. Pedestrian Attribute Recognition with Graph Convolutional Network in Surveillance Scenarios. Future Internet 2019, 11, 245 .

AMA Style

Xiangpeng Song, HongBin Yang, Congcong Zhou. Pedestrian Attribute Recognition with Graph Convolutional Network in Surveillance Scenarios. Future Internet. 2019; 11 (11):245.

Chicago/Turabian Style

Xiangpeng Song; HongBin Yang; Congcong Zhou. 2019. "Pedestrian Attribute Recognition with Graph Convolutional Network in Surveillance Scenarios." Future Internet 11, no. 11: 245.

Journal article
Published: 07 June 2018 in Future Internet
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Provable Data Possession (PDP) protocol makes it possible for cloud users to check whether the cloud servers possess their original data without downloading all the data. However, most of the existing PDP schemes are based on either public key infrastructure (PKI) or identity-based cryptography, which will suffer from issues of expensive certificate management or key escrow. In this paper, we propose a new construction of certificateless provable group shared data possession (CL-PGSDP) protocol by making use of certificateless cryptography, which will eliminate the above issues. Meanwhile, by taking advantage of zero-knowledge protocol and randomization method, the proposed CL-PGSDP protocol leaks no information of the stored data and the group user’s identity to the verifiers during the verifying process, which is of the property of comprehensive privacy preservation. In addition, our protocol also supports efficient user revocation from the group. Security analysis and experimental evaluation indicate that our CL-PGSDP protocol provides strong security with desirable efficiency.

ACS Style

HongBin Yang; Shuxiong Jiang; Wenfeng Shen; Zhou Lei. Certificateless Provable Group Shared Data Possession with Comprehensive Privacy Preservation for Cloud Storage. Future Internet 2018, 10, 49 .

AMA Style

HongBin Yang, Shuxiong Jiang, Wenfeng Shen, Zhou Lei. Certificateless Provable Group Shared Data Possession with Comprehensive Privacy Preservation for Cloud Storage. Future Internet. 2018; 10 (6):49.

Chicago/Turabian Style

HongBin Yang; Shuxiong Jiang; Wenfeng Shen; Zhou Lei. 2018. "Certificateless Provable Group Shared Data Possession with Comprehensive Privacy Preservation for Cloud Storage." Future Internet 10, no. 6: 49.