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为提高人体下肢步态相识别的准确性,研究了融合表面肌电信号(sEMG)、膝关节角度和足底压力信号的人体下肢步态相识别方法。首先, 将sEMG信号进行小波包分解提取多尺度能量和多尺度模糊熵特征;然后,对提取的sEMG信号特征值采用主成分分析(PCA)方法进行降维处理,并与足底压力特征值和膝关节能量特征值构成一组特征向量;最后,将特征向量输入粒子群优化最小二乘支持向量机(PSO-LSSVM)对人体下肢运动信息进行步态相识别。实验结果表明,所提方法相较于其他方法有较高的识别准确率和有效性。 相似文献
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《中国计量学院学报》2017,(2)
针对单一特征步态识别率低的问题,提出一种将步态能量图(Gait Energy Image,GEI)中动态部分和Gabor小波特征融合的步态识别算法.首先,通过运动目标检测及二值化和形态学处理等预处理操作得到步态轮廓图,再进一步从步态轮廓图计算得到步态能量图,并从中分割出动态部分.然后,利用Gabor小波从步态能量图的动态部分中提取不同角度的信息,将两步态特征融合在一起,对融合后得到的特征向量用改进的KPCA方法进行降维.最后,将降维后的融合特征向量输入到基于多分类的支持向量机(Support Vector Machine,SVM)中,从而完成步态的分类和识别.经过在中国科学院自动化研究所CASIA步态数据库上进行实验,取得了很好的识别效果,实验结果表明,与单一特征的步态识别方法相比,融合后算法的识别率提高了约10%. 相似文献
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传统的基于方向梯度直方图与支持向量机的行人检测方法运算量大,针对这一问题,本文从轮廓特征的角度出发,提出了头肩轮廓特征与神经网络相结合的检测方法。该方法根据人体头肩模型具有相对稳定性,且轮廓特征可以作为人体识别的依据,采用边缘检测与均值漂移相结合的方式提取人体轮廓,采用经PCA降维的傅里叶描述子提取轮廓特征,结合神经网络分类器完成初次人体识别。采用RGB头发模型和均值漂移方法,对遮挡情况下被判别为非人体的目标图像做进一步处理,聚类出多个人体头肩模型,重新参与分类。实验结果表明,本方法人体检测的准确率和检测速度与现有的算法相比都有所提高,且克服了遮挡情况下人体头肩模型提取错误的弊端,提高了人体检测的识别率和应用范围。 相似文献
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视频监控中基于在线多核学习的目标再现识别 总被引:1,自引:0,他引:1
在非重叠多摄像机或单摄像机视频监控中,识别跟踪目标的再次出现很重要.针对传统支持向量机方法在特征融合方面的缺陷,本文提出了一种新的基于在线多核学习的人体目标再现识别方法.该方法对跟踪目标视频前景图像序列提取具有互补性的视觉单词树直方图和全局颜色直方图二种特征,再采用多核学习方法在线训练人体目标视觉外观,从而得到多核特征融合模型.实验结果表明,该方法能快速训练人体目标外观模型,满足视频监控的实时要求,多核融合模型获得了比单一特征模型和单核支持向量机方法更高的识别性能. 相似文献
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步态识别作为一种新的生物特征识别技术,是通过人走路的姿势实现对个人身份的识别和认证。对隐马尔可夫模型进行研究并对算法实现中遇到的问题加以分析,在此基础上实现了利用隐马尔可夫模型的步态特征提取与身份识别方法。 相似文献
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《中国计量学院学报》2016,(2):216-222
为了有效地获取步态连续性的动态特征,快速准确地进行身份识别.特提出了一个基于步态能量图(Gait Energy Image,GEI)和核Fisher判别分析(Kernel-based Fisher Discrimination Analysis,KFDA)的分类识别算法.算法首先以步态能量图(GEI)按列向量作为输入,求得最优子空间W_(opt)和α_(opt).利用提取步态能量图(GEI)的步态信息向量计算在α_(opt)上的投影,并计算其投影轨迹.在分类阶段,采用最近邻分类器(Nearest neighbor classifier).最终在中科院自动化研究所CASIA B步态数据库上进行实验,对比多项式、高斯径向基核函数和其他四种算法的结果显示,本文算法取得了较高的识别率. 相似文献
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WMFVR身份识别及应用 总被引:1,自引:1,他引:0
提出一种身份识别模型WMFVR(加权多特征视频识别)。模型分三层:先验层,建立识别群体视频样本库,通过数据发掘可识别(区分)的视频特征点,如身高、肩宽、人脸等,每个特征点聚类建立特征分布,如按身高聚为高、中、低三类;识别层,在相对静止背景下获取连续图像,检测跟踪运动目标,重建人体三维运动,按时间序列识别各个特征点,结合先验知识库,加权决策识别结果:后验层,根据识别结果,更新视频特征分布,刷新先验知识库。与传统视频身份识别相比,WMFVR充分利用连续动态图像下的视频特征,并引入后验机制,提高了视频识别的可应用性。基于WMFVR设计的视频考勤系统识别率为94.872%,表明在固定场景小范围人群的身份识别中具有良好的鲁棒性。 相似文献
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Gait recognition using active shape model and motion prediction 总被引:1,自引:0,他引:1
This study presents a novel, robust gait recognition algorithm for human identification from a sequence of segmented noisy silhouettes in a low-resolution video. The proposed recognition algorithm enables automatic human recognition from model-based gait cycle extraction based on the prediction-based hierarchical active shape model (ASM). The proposed algorithm overcomes drawbacks of existing works by extracting a set of relative model parameters instead of directly analysing the gait pattern. The feature extraction function in the proposed algorithm consists of motion detection, object region detection and ASM, which alleviate problems in the baseline algorithm such as background generation, shadow removal and higher recognition rate. Performance of the proposed algorithm has been evaluated by using the HumanID Gait Challenge data set, which is the largest gait benchmarking data set with 122 objects with different realistic parameters including viewpoint, shoe, surface, carrying condition and time. 相似文献
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Malicious social robots are the disseminators of malicious information on social
networks, which seriously affect information security and network environments. Efficient
and reliable classification of social robots is crucial for detecting information manipulation
in social networks. Supervised classification based on manual feature extraction has been
widely used in social robot detection. However, these methods not only involve the privacy
of users but also ignore hidden feature information, especially the graph feature, and the
label utilization rate of semi-supervised algorithms is low. Aiming at the problems of
shallow feature extraction and low label utilization rate in existing social network robot
detection methods, in this paper a robot detection scheme based on weighted network
topology is proposed, which introduces an improved network representation learning
algorithm to extract the local structure features of the network, and combined with the
graph convolution network (GCN) algorithm based on the graph filter, to obtain the global
structure features of the network. An end-to-end semi-supervised combination model
(Semi-GSGCN) is established to detect malicious social robots. Experiments on a social
network dataset (cresci-rtbust-2019) show that the proposed method has high versatility and
effectiveness in detecting social robots. In addition, this method has a stronger insight into
robots in social networks than other methods. 相似文献
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Xinliang Tang Xing Sun Zhenzhou Wang Pingping Yu Ning Cao Yunfeng Xu 《计算机、材料和连续体(英文)》2020,64(2):1185-1198
The appearance of pedestrians can vary greatly from image to image, and
different pedestrians may look similar in a given image. Such similarities and variabilities
in the appearance and clothing of individuals make the task of pedestrian re-identification
very challenging. Here, a pedestrian re-identification method based on the fusion of local
features and gait energy image (GEI) features is proposed. In this method, the human
body is divided into four regions according to joint points. The color and texture of each
region of the human body are extracted as local features, and GEI features of the
pedestrian gait are also obtained. These features are then fused with the local and GEI
features of the person. Independent distance measure learning using the cross-view
quadratic discriminant analysis (XQDA) method is used to obtain the similarity of the
metric function of the image pairs, and the final similarity is acquired by weight
matching. Evaluation of experimental results by cumulative matching characteristic
(CMC) curves reveals that, after fusion of local and GEI features, the pedestrian reidentification effect is improved compared with existing methods and is notably better
than the recognition rate of pedestrian re-identification with a single feature. 相似文献
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Todd C. Pataky Tingting Mu Kerstin Bosch Dieter Rosenbaum John Y. Goulermas 《Journal of the Royal Society Interface》2012,9(69):790-800
Everyone''s walking style is unique, and it has been shown that both humans and computers are very good at recognizing known gait patterns. It is therefore unsurprising that dynamic foot pressure patterns, which indirectly reflect the accelerations of all body parts, are also unique, and that previous studies have achieved moderate-to-high classification rates (CRs) using foot pressure variables. However, these studies are limited by small sample sizes (n < 30), moderate CRs (CR ≃ 90%), or both. Here we show, using relatively simple image processing and feature extraction, that dynamic foot pressures can be used to identify n = 104 subjects with a CR of 99.6 per cent. Our key innovation was improved and automated spatial alignment which, by itself, improved CR to over 98 per cent, a finding that pointedly emphasizes inter-subject pressure pattern uniqueness. We also found that automated dimensionality reduction invariably improved CRs. As dynamic pressure data are immediately usable, with little or no pre-processing required, and as they may be collected discreetly during uninterrupted gait using in-floor systems, foot pressure-based identification appears to have wide potential for both the security and health industries. 相似文献
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针对故障诊断中采用EMD方法存在模态混叠现象,引起故障特征提取精度低的问题。提出了一种解相关多频率经验模态分解(Decorrelation Multiple-Frequency Empirical Mode Decomposition,DMFEMD)方法,首先对初始信号添加多个频率的掩蔽信号,初步分解其中不同频率比的信号分量得到多个IMF分量;其次计算相邻IMF之间的相关系数并对其解耦,进一步分离IMF中存在混叠的部分,得到最优IMF;最终,从原始信号中减去最优IMF,然后重复上述步骤,直到残余分量为常数或单调。由于保证了IMF之间互不相关且互不干扰,因此模态混叠现象显著减弱,有效提高故障特征提取精度。利用排列熵算法对一系列最优IMF构造特征样本集,引入SVM建立故障分类模型,实现设备故障诊断。通过试验证明,DMFEMD与传统的方法相比,能有效分离不同频率比混合信号,提高分解效果。同时以轴承振动信号为例,DMFEMD可以更好的提取轴承的故障特征,结合PE与SVM能够实现不同故障类型的高效精确的诊断。 相似文献
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Time series classification (TSC) has attracted various attention in the
community of machine learning and data mining and has many successful applications
such as fault detection and product identification in the process of building a smart
factory. However, it is still challenging for the efficiency and accuracy of classification
due to complexity, multi-dimension of time series. This paper presents a new approach
for time series classification based on convolutional neural networks (CNN). The
proposed method contains three parts: short-time gap feature extraction, multi-scale local
feature learning, and global feature learning. In the process of short-time gap feature
extraction, large kernel filters are employed to extract the features within the short-time
gap from the raw time series. Then, a multi-scale feature extraction technique is applied
in the process of multi-scale local feature learning to obtain detailed representations. The
global convolution operation with giant stride is to obtain a robust and global feature
representation. The comprehension features used for classifying are a fusion of short time
gap feature representations, local multi-scale feature representations, and global feature
representations. To test the efficiency of the proposed method named multi-scale feature
fusion convolutional neural networks (MSFFCNN), we designed, trained MSFFCNN on
some public sensors, device, and simulated control time series data sets. The comparative
studies indicate our proposed MSFFCNN outperforms other alternatives, and we also
provided a detailed analysis of the proposed MSFFCNN. 相似文献