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李中益, 杨观赐, 李杨, 何玲. 基于图像语义的服务机器人视觉隐私行为识别与保护系统[J]. 计算机辅助设计与图形学学报, 2020, 32(10): 1679-1687. DOI: 10.3724/SP.J.1089.2020.18072
引用本文: 李中益, 杨观赐, 李杨, 何玲. 基于图像语义的服务机器人视觉隐私行为识别与保护系统[J]. 计算机辅助设计与图形学学报, 2020, 32(10): 1679-1687. DOI: 10.3724/SP.J.1089.2020.18072
Li Zhongyi, Yang Guanci, Li Yang, He Ling. Visual Privacy Behavior Recognition and Protection System Based on Image Semantics for Social Robot[J]. Journal of Computer-Aided Design & Computer Graphics, 2020, 32(10): 1679-1687. DOI: 10.3724/SP.J.1089.2020.18072
Citation: Li Zhongyi, Yang Guanci, Li Yang, He Ling. Visual Privacy Behavior Recognition and Protection System Based on Image Semantics for Social Robot[J]. Journal of Computer-Aided Design & Computer Graphics, 2020, 32(10): 1679-1687. DOI: 10.3724/SP.J.1089.2020.18072

基于图像语义的服务机器人视觉隐私行为识别与保护系统

Visual Privacy Behavior Recognition and Protection System Based on Image Semantics for Social Robot

  • 摘要: 为了平衡摄像头使用与用户隐私泄露间的矛盾从数据源上解决隐私泄露问题,提出基于图像语义的服务机器人视觉隐私行为识别与保护算法并集成开发隐私保护系统.首先将注意力模型与卷积神经网络结合,提出融合注意力模型的卷积神经网络场景识别算法;然后设计基于Inception-v3和长短期记忆网络的涉隐私图像语义特征描述算法,进而形成基于图像语义的服务机器人视觉隐私行为识别与保护算法.以室内场景数据集MIT Scene-67为训练和测试数据集,与4种算法比较的实验结果表明,融合注意力模型的卷积神经网络场景识别算法在识别准确性和鲁棒性方面优于比较算法.所实现的视觉隐私行为识别与保护系统性能测试结果表明,系统平均隐私识别准确率和隐私场景语义描述正确率分别为91.65%和86.60%,表现出良好的鲁棒性和隐私保护性能.

     

    Abstract: In order to balance the contradiction between camera using and privacy disclosure and address the problem of privacy disclosure from data sources,we proposed the visual privacy behavior recognition and protection algorithm for social robot based on image semantics,and developed a privacy protection system.Firstly,the scene recognition algorithm based on convolution neural network and attention model were proposed by introducing the attention model into the convolution neural network.Then the privacy-related images semantic description algorithm based on Inception-v3 and long short-term memory network was designed.Then the visual privacy behavior recognition and protection algorithm for social robot based on image semantics was formed.The indoor scene dataset MIT Scene-67 was used as the train and test datasets,and the comparison results with four algorithms show that the proposed algorithm is more superior in the aspect of recognition accuracy and robustness.The results show that the average privacy recognition accuracy and the privacy scene semantic description accuracy are 91.65%and 86.60%respectively,which indicate that the system has acceptable robustness and privacy protection performance.

     

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