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第40届中国控制会议

作者： Ru Zeng Yan Song Chongjing Wang Yanfang Chu Department of Control Science and Engineering University of Shanghai for Science and Technology Informatization and Industrialization Integration Research Institute the China Academy of Information and Communications Technology Shanghai Marine Equipment Research Institute

Capsule network（CapsNet） has received a special concern recently owing to its great capability of guaranteeing the model robustness against the affine transformation and adversarial images. However, most CapsNets perform poor while costing huge at image classification on complex datasets, due probably to the weak feature extraction, the gradient problems and many intermediate variables employed in dynamic routing. To address this issue, a novel CapsNet called dense CapsNet（denseCap） is proposed. First, dense blocks with short-cuts are stacked before capsule layers to adequately extract abundant essential features from image input while avoiding vanishing gradient. Then, CapsNet ReLU（CapsReLU） function is put forward to squash the capsule such that the sensitivity to the parameter initiation and vanishing gradient are handled. Furthermore, the influence of various dimensions of capsules on the reconstructed images is carefully studied. Finally, empirical results on five benchmark datasets show that the proposed denseCaps outperform state-of-the-art capsule models.

关键词： Capsule network image classification dense blocks ReLU activation function vanishing gradient

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dense Spatial-Temporal Graph Convolutional Network Based on Lightweight OpenPose for Detecting Falls

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Computers, Materials & Continua 2023年第10期77卷 47-61页

作者： Xiaorui Zhang Qijian Xie Wei Sun Yongjun Ren Mithun Mukherjee School of Computer Science Nanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China School of Artificial Intelligence Nanjing University of Information Science&TechnologyNanjing210044China

Fall behavior is closely related to high mortality in the elderly,so fall detection becomes an important and urgent research area.However,the existing fall detection methods are difficult to be applied in daily life due to a large amount of calculation and poor detection accuracy.To solve the above problems,this paper proposes a dense spatial-temporal graph convolutional network based on lightweight OpenPose.Lightweight OpenPose uses MobileNet as a feature extraction network,and the prediction layer uses bottleneck-asymmetric structure,thus reducing the amount of the network.The bottleneck-asymmetrical structure compresses the number of input channels of feature maps by 1×1 convolution and replaces the 7×7 convolution structure with the asymmetric structure of 1×7 convolution,7×1 convolution,and 7×7 convolution in parallel.The spatial-temporal graph convolutional network divides the multi-layer convolution into dense blocks,and the convolutional layers in each dense block are connected,thus improving the feature transitivity,enhancing the network’s ability to extract features,thus improving the detection accuracy.Two representative datasets,Multiple Cameras Fall dataset(MCF),and Nanyang Technological University Red Green Blue+Depth Action Recognition dataset(NTU RGB+D),are selected for our experiments,among which NTU RGB+D has two evaluation benchmarks.The results show that the proposed model is superior to the current fall detection models.The accuracy of this network on the MCF dataset is 96.3%,and the accuracies on the two evaluation benchmarks of the NTU RGB+D dataset are 85.6%and 93.5%,respectively.

关键词： Fall detection lightweight OpenPose spatial-temporal graph convolutional network dense blocks

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