Visual privacy behaviour recognition for social robots based on an improved generative adversarial network

IF 1.5 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE IET Computer Vision Pub Date : 2023-08-04 DOI:10.1049/cvi2.12231
Guanci Yang, Jiacheng Lin, Zhidong Su, Yang Li
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Abstract

Although social robots equipped with visual devices may leak user information, countermeasures for ensuring privacy are not readily available, making visual privacy protection problematic. In this article, a semi-supervised learning algorithm is proposed for visual privacy behaviour recognition based on an improved generative adversarial network for social robots; it is called PBR-GAN. A 9-layer residual generator network enhances the data quality, and a 10-layer discriminator network strengthens the feature extraction. A tailored objective function, loss function, and strategy are proposed to dynamically adjust the learning rate to guarantee high performance. A social robot platform and architecture for visual privacy recognition and protection are implemented. The recognition accuracy of the proposed PBR-GAN is compared with Inception_v3, SS-GAN, and SF-GAN. The average recognition accuracy of the proposed PBR-GAN is 85.91%, which is improved by 3.93%, 9.91%, and 1.73% compared with the performance of Inception_v3, SS-GAN, and SF-GAN respectively. Through a case study, seven situations are considered related to privacy at home, and develop training and test datasets with 8,720 and 1,280 images, respectively, are developed. The proposed PBR-GAN recognises the designed visual privacy information with an average accuracy of 89.91%.

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基于改进生成对抗网络的社交机器人视觉隐私行为识别
尽管配备了视觉设备的社交机器人可能会泄露用户信息,但确保隐私的对策并不容易获得,这使得视觉隐私保护成为问题。本文提出了一种基于改进的社交机器人生成对抗性网络的视觉隐私行为识别半监督学习算法;它被称为PBR-GAN。9层残差生成器网络增强了数据质量,10层鉴别器网络增强了特征提取。提出了一种定制的目标函数、损失函数和策略来动态调整学习率,以确保高性能。实现了一个用于视觉隐私识别和保护的社交机器人平台和架构。将所提出的PBR‐GAN的识别精度与Inception_v3、SS‐GAN和SF‐GAN进行了比较。所提出的PBR-GAN的平均识别准确率为85.91%,与Inception_v3、SS‐GAN和SF‐GAN的性能相比,分别提高了3.93%、9.91%和1.73%。通过案例研究,考虑了七种与家庭隐私有关的情况,并分别开发了8720张和1280张图像的训练和测试数据集。所提出的PBR-GAN识别设计的视觉隐私信息的平均准确率为89.91%。
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来源期刊
IET Computer Vision
IET Computer Vision 工程技术-工程:电子与电气
CiteScore
3.30
自引率
11.80%
发文量
76
审稿时长
3.4 months
期刊介绍: IET Computer Vision seeks original research papers in a wide range of areas of computer vision. The vision of the journal is to publish the highest quality research work that is relevant and topical to the field, but not forgetting those works that aim to introduce new horizons and set the agenda for future avenues of research in computer vision. IET Computer Vision welcomes submissions on the following topics: Biologically and perceptually motivated approaches to low level vision (feature detection, etc.); Perceptual grouping and organisation Representation, analysis and matching of 2D and 3D shape Shape-from-X Object recognition Image understanding Learning with visual inputs Motion analysis and object tracking Multiview scene analysis Cognitive approaches in low, mid and high level vision Control in visual systems Colour, reflectance and light Statistical and probabilistic models Face and gesture Surveillance Biometrics and security Robotics Vehicle guidance Automatic model aquisition Medical image analysis and understanding Aerial scene analysis and remote sensing Deep learning models in computer vision Both methodological and applications orientated papers are welcome. Manuscripts submitted are expected to include a detailed and analytical review of the literature and state-of-the-art exposition of the original proposed research and its methodology, its thorough experimental evaluation, and last but not least, comparative evaluation against relevant and state-of-the-art methods. Submissions not abiding by these minimum requirements may be returned to authors without being sent to review. Special Issues Current Call for Papers: Computer Vision for Smart Cameras and Camera Networks - https://digital-library.theiet.org/files/IET_CVI_SC.pdf Computer Vision for the Creative Industries - https://digital-library.theiet.org/files/IET_CVI_CVCI.pdf
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