共查询到18条相似文献,搜索用时 187 毫秒
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基于新时空融合的步态轮廓分割算法 总被引:1,自引:0,他引:1
从人体步态图像视频序列中,提取完整的人体区域是人体运动步态识别的一个重要环节.提出一种新的人体运动目标分割算法,无需小波反变换.结合背景减除法和帧间差分法所得到的二值结果来进行运动估计,对当前帧图像采用一阶小波变换,利用高阶线性插值算法将小波变换的LL分量扩展与当前帧图像同样的大小,采用分水岭分割算法把扩展后的LL分量图像分割成许多封闭而不重叠的小区域(空域分割),进行时空融合.可以在NLPR步态数据库中进行实验,结果表明,算法能够精确地识别完整的人体区域,拥有良好的抗噪性和适应性,进一步提高识别率. 相似文献
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步态周期是步态的一个重要特征,步态识别是建立在准确的步态周期分割之上的。本文提出了一个基于下肢轮廓的步态周期检测方法,首先对步态序列图像进行灰度化,然后计算各像素点在步态图像序列中的中值,获取整个步态序列图像的背景,提取人体目标后,利用数学形态学方法和区域跟踪算法填补二值化图像中的空洞;采用轮廓跟踪算法获得人体下肢轮廓,并将其转换为对应距离向量,在一个步态系列中利用距离向量范数研究步态周期。本算法计复杂度低,鲁棒性好,精确度高。 相似文献
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研究步态识别问题,针对在当前二维步态识别系统中,识别过程仅仅针对灰度、平面几何距离等二维特征信息,忽略了人体走路时的三维步态特征,步态识别准确度不高的问题.提出了一种加入三维参数的步态识别算法.利用摄像机采集单帧步态图像序列,利用身体结构的知识和摄像机标定的知识提取出人体走步时的人体三维特征数据,利用提取出二维和三维的步态特征,进行步态识别.结果表明相对于以二维步态特征为参数的步态识别,识别率有了明显改进. 相似文献
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步态识别作为一种新的生物识别技术,通过人走路的姿势实现对个人身份的识别和认证。步态特征提取是步态识别的关键步骤。采用背景消减法与对称差分法相结合对运动人体分割,采用改进的GVF Snake模型对人体运动步态轮廓进行边缘提取。实验结果表明该方法能准确高效地提取边缘特征作为步态识别的特征。 相似文献
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基于显著性轮廓的苹果目标识别方法 总被引:1,自引:0,他引:1
正确地将苹果从图像中识别出来是苹果采摘机器人实现自动采摘的前提,为了完整地提取苹果目标轮廓,提高识别率,提出一种基于显著性轮廓的苹果目标识别方法。首先利用K-means无监督聚类算法对苹果图像进行分割,将图像分为背景和苹果目标区域;由于光照等因素,苹果目标区域内部存在大面积空洞,引入ASIFT特征,将完整的苹果目标与存在空洞的苹果目标进行ASIFT特征匹配记录与空洞相对应的特征,由这些特征恢复成像素填补空洞,初步得到轮廓不完整的分割目标;然后在基于区域的基础上,采用gPb轮廓检测器对苹果目标图像进行轮廓检测生成较长、较明显的灰度轮廓;继而利用动态阈值OTSU法对灰度轮廓进行自动阈值处理,去除苹果目标周围大量的边缘噪声,确定连续的显著性轮廓,有效地弥补了K-means算法无法精确提取轮廓的缺陷,最终实现完整地提取苹果目标。本文方法取得的平均目标识别率在98%以上,多组实验结果均验证了本文方法的有效性和可靠性。 相似文献
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彩色图像序列中运动人体轮廓提取 总被引:3,自引:0,他引:3
在视频序列的人体运动分析中,实时提取出运动人体轮廓,是很多研究起始的关键步骤.而彩色图像由于具有比灰度图像更多的视觉信息,受到了越来越多的重视.采用了一种新的色彩背景模型;运用改进的背景差分方法在复杂背景下获得运动人体的轮廓.实验结果表明上述算法对噪声抑制和人体图像断裂处填充都是有效的,在目标物是运动物体,且背景绝大多数均为静止时,该算法适用,能够实时提取出运动人体的轮廓. 相似文献
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根据人体步态变化特点,提出一种基于特征融合和神经网络的步态识别算法。首先采用时域差分法对运动人体轮廓进行分割,然后分别提取空间特征和频率特征,将两步态特征融合在一起,从而实现步态的分类和识别。在CASIA步态数据库上进行仿真实验,仿真结果表明,该方法不仅克服了单一特征提取方法存在的缺陷,同时提高了步态识别正确率。 相似文献
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Recognizing people by gait promises to be useful for identifying individuals from a distance; in this regard, improved techniques
are under development. In this paper, an improved method for gait recognition is proposed. Binarized silhouette of a motion
object is first represented by four 1-D signals that are the basic image features called the distance vectors. The distance
vectors are differences between the bounding box and silhouette, and extracted using four projections to silhouette. Fourier
Transform is employed as a preprocessing step to achieve translation invariant for the gait patterns accumulated from silhouette
sequences that are extracted from the subjects’ walk in different speed and/or different time. Then, eigenspace transformation
is applied to reduce the dimensionality of the input feature space. Support vector machine (SVM)-based pattern classification
technique is then performed in the lower-dimensional eigenspace for recognition. The input feature space is alternatively
constructed by using two different approaches. The four projections (1-D signals) are independently classified in the first
approach. A fusion task is then applied to produce the final decision. In the second approach, the four projections are concatenated
to have one vector and then pattern classification with one vector is performed in the lower-dimensional eigenspace for recognition.
The experiments are carried out on the most well-known public gait databases: the CMU, the USF, SOTON, and NLPR human gait
databases. To effectively understand the performance of the algorithm, the experiments are executed and presented as increasing
amounts of the gait cycles of each person available during the training procedure. Finally, the performance of the proposed
algorithm is comparatively illustrated to take into consideration the published gait recognition approaches. 相似文献
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Silhouette analysis-based gait recognition for human identification 总被引:24,自引:0,他引:24
Liang Wang Tieniu Tan Huazhong Ning Weiming Hu 《IEEE transactions on pattern analysis and machine intelligence》2003,25(12):1505-1518
Human identification at a distance has recently gained growing interest from computer vision researchers. Gait recognition aims essentially to address this problem by identifying people based on the way they walk. In this paper, a simple but efficient gait recognition algorithm using spatial-temporal silhouette analysis is proposed. For each image sequence, a background subtraction algorithm and a simple correspondence procedure are first used to segment and track the moving silhouettes of a walking figure. Then, eigenspace transformation based on principal component analysis (PCA) is applied to time-varying distance signals derived from a sequence of silhouette images to reduce the dimensionality of the input feature space. Supervised pattern classification techniques are finally performed in the lower-dimensional eigenspace for recognition. This method implicitly captures the structural and transitional characteristics of gait. Extensive experimental results on outdoor image sequences demonstrate that the proposed algorithm has an encouraging recognition performance with relatively low computational cost. 相似文献
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基于贝叶斯网络的步态识别 总被引:2,自引:0,他引:2
步态作为一种重要的生物特征由于其远距离身份识别能力而逐渐受到人们的重视。本文提出了一种基于贝叶斯网络的步态识别方法。首先应用背景差方法获得运动人体侧面二值图像,将侧面像分为七部分来提取特征,采用最大方差法对训练集进行离散化,对各部分分别建立贝叶斯网络,最后利用“投票”规则将网络推理结果进行组合。将该方法在Soton步态数据库上进行试验,取得了比较理想的识别效果。 相似文献
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Conventional gait recognition schemes has poor recognition accuracies in presence of covariates. It is mainly due to ineffective and inefficient representation and discriminative feature extraction schemes. The paper presents new technique to extract discriminative features from masked gait energy image based on curvelet transform and PCANet. The binary gait silhouette video sequence obtained from pre-processing of video sequence is converted in to masked gait energy image and then direction and edge representation ability of fast discrete curvelet transform is employed. Nonlinear and non invertible, image space to feature space mapping scheme of PCANet is used to extract discriminative robust features. The suitability and effectiveness of newly proposed scheme is demonstrated by experimentation on standard publicly available benchmark USF HumanID database. 相似文献
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This paper presents a novel approach for human identification at a distance using gait recognition. Recognition of a person from their gait is a biometric of increasing interest. The proposed work introduces a nonlinear machine learning method, kernel Principal Component Analysis (PCA), to extract gait features from silhouettes for individual recognition. Binarized silhouette of a motion object is first represented by four 1-D signals which are the basic image features called the distance vectors. Fourier transform is performed to achieve translation invariant for the gait patterns accumulated from silhouette sequences which are extracted from different circumstances. Kernel PCA is then used to extract higher order relations among the gait patterns for future recognition. A fusion strategy is finally executed to produce a final decision. The experiments are carried out on the CMU and the USF gait databases and presented based on the different training gait cycles. 相似文献
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基于核主成分分析的步态识别方法 总被引:2,自引:0,他引:2
为了从多帧步态序列中更有效地提取步态特征并实时性地进行身份识别,提出一种有效的基于平均步态能量图(MGEI)的核主成分分析(KPCA)的身份识别方法。通过预处理技术提取出运动人体的侧面轮廓,根据步态下肢的摆动距离统计出步态周期,得到MGEI。KPCA采用非线性方法提取主成分,描述待识别图像中多个像素之间的相关性。利用KPCA的方法在高维空间对MGEI提取特征,选择合适的核函数,用方差倒数加权欧氏距离进行身份识别。实验结果表明,该算法具有较好的识别性能,并且耗时大大缩短。 相似文献