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 d...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.展开更多
To address the problems of lack of high-frequency information and texture details and unstable training in superresolution generative adversarial net-works,this paper optimizes the generator and discriminator based on...To address the problems of lack of high-frequency information and texture details and unstable training in superresolution generative adversarial net-works,this paper optimizes the generator and discriminator based on the SRGAN model.First,the residual dense block is used as the basic structural unit of the gen-erator to improve the network’s feature extraction capability.Second,enhanced lightweight coordinate attention is incorporated to help the network more precisely concentrate on high-frequency location information,thereby allowing the gener-ator to produce more realistic image reconstruction results.Then,we propose a symmetric and efficient pyramidal segmentation attention discriminator network in which the attention mechanism is capable of derivingfiner-grained multiscale spatial information and creating long-term dependencies between multiscale chan-nel attentions,thus enhancing the discriminative ability of the network.Finally,a Charbonnier loss function and a gradient variance loss function with improved robustness are used to better realize the image’s texture structure and enhance the model’s stability.Thefindings from the experiments reveal that the reconstructed image quality enhances the average peak signal-to-noise ratio(PSNR)by 1.59 dB and the structural similarity index(SSIM)by 0.045 when compared to SRGAN on the three test sets.Compared with the state-of-the-art methods,the reconstructed images have a clearer texture structure,richer high-frequency details,and better visual effects.展开更多
针对网络视频质量低导致人脸检测准确率低的问题,提出一种基于人脸超分辨率重建的SR Face Detection模型。使用去掉自监督分支且以Resnet50为基础网络的RetinaFace进行帧图片人脸的粗提取;在人脸检测器后增加一个人脸超分辨率重建网络,...针对网络视频质量低导致人脸检测准确率低的问题,提出一种基于人脸超分辨率重建的SR Face Detection模型。使用去掉自监督分支且以Resnet50为基础网络的RetinaFace进行帧图片人脸的粗提取;在人脸检测器后增加一个人脸超分辨率重建网络,剔除粗提取人脸中的非人脸。该超分网络的生成网络使用残差密集块进行特征提取,加入注意力损失和热图,更好地还原面部细节;根据实际需求设计一个多判别功能的判别网络。实验结果表明,SR Face Detection模型在WIDER FACE数据集上取得了令人信服的结果,提高了人脸检测准确率,且人脸检测场景越复杂,效果提升越明显。展开更多
基金supported,in part,by the National Nature Science Foundation of China under Grant Numbers 62272236,62376128in part,by the Natural Science Foundation of Jiangsu Province under Grant Numbers BK20201136,BK20191401.
文摘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.
基金This work was supported in part by the Basic Scientific Research Project of Liaoning Provincial Department of Education under Grant Nos.LJKQZ2021152 and LJ2020JCL007in part by the National Science Foundation of China(NSFC)under Grant No.61602226in part by the PhD Startup Foundation of Liaoning Technical University of China under Grant Nos.18-1021.
文摘To address the problems of lack of high-frequency information and texture details and unstable training in superresolution generative adversarial net-works,this paper optimizes the generator and discriminator based on the SRGAN model.First,the residual dense block is used as the basic structural unit of the gen-erator to improve the network’s feature extraction capability.Second,enhanced lightweight coordinate attention is incorporated to help the network more precisely concentrate on high-frequency location information,thereby allowing the gener-ator to produce more realistic image reconstruction results.Then,we propose a symmetric and efficient pyramidal segmentation attention discriminator network in which the attention mechanism is capable of derivingfiner-grained multiscale spatial information and creating long-term dependencies between multiscale chan-nel attentions,thus enhancing the discriminative ability of the network.Finally,a Charbonnier loss function and a gradient variance loss function with improved robustness are used to better realize the image’s texture structure and enhance the model’s stability.Thefindings from the experiments reveal that the reconstructed image quality enhances the average peak signal-to-noise ratio(PSNR)by 1.59 dB and the structural similarity index(SSIM)by 0.045 when compared to SRGAN on the three test sets.Compared with the state-of-the-art methods,the reconstructed images have a clearer texture structure,richer high-frequency details,and better visual effects.
文摘针对工业场景下图像模糊、分辨率低、边缘细节不明显等问题,提出一种基于生成对抗网络的低质图像增强算法。首先,设计退化网络获得与真实场景更为接近的低质图像,以此与现实高清图像获得特征映射关系;其次,在使用密集残差块(residual in residual dense block,RRDB)的基础上添加卷积注意力模块,增强RRDB网络的特征表达能力,以有效地捕获关键特征信息;最后,设计边缘增强网络模块结合改进的RRDB作为生成器,图像细节信息的捕捉与还原能力得到显著提升,并与判别器对抗生成更高质量的图像。实验结果表明,相较于现有常用的图像增强算法,所提算法能有效提升工业场景图像清晰度、保留图像细节并减少失真。定量指标峰值信噪比平均提升10.45%,结构相似性平均提升15.92%,运行速度快,能满足工业生产需求。
文摘针对网络视频质量低导致人脸检测准确率低的问题,提出一种基于人脸超分辨率重建的SR Face Detection模型。使用去掉自监督分支且以Resnet50为基础网络的RetinaFace进行帧图片人脸的粗提取;在人脸检测器后增加一个人脸超分辨率重建网络,剔除粗提取人脸中的非人脸。该超分网络的生成网络使用残差密集块进行特征提取,加入注意力损失和热图,更好地还原面部细节;根据实际需求设计一个多判别功能的判别网络。实验结果表明,SR Face Detection模型在WIDER FACE数据集上取得了令人信服的结果,提高了人脸检测准确率,且人脸检测场景越复杂,效果提升越明显。
