基于图像序列的人体步态识别方法研究
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摘要
随着现代社会的发展和人们安全意识的提高,越来越多的重要场合,如车站、机场、银行、政府部门、居民社区等,都需要对人的身份进行鉴别。生物特征识别是一种利用人的生理或行为的特征来进行人身份识别的技术,它较之传统的身份识别方法更为准确和快速。常用于身份识别的生物特征有:人耳、手形、指纹、掌纹、人脸、虹膜、语音以及签名等等。步态识别是一种新兴的生物特征识别技术。步态特征主要反映人行走的姿势,较之其他生物识别技术,步态识别具有在远距离接触或低质量视频的状态下进行人身份鉴别等优点,因此当前人体步态识别已受到越来越多研究者的关注。鉴于步态识别的优点以及它具有的重要研究意义和实际应用价值,本文对其进行了深入的研究,主要的研究工作和成果如下:
(1)提出了基于多区域形状特征的步态识别方法。该方法首先将图像序列中每帧步态侧影图像划分为若干子区域,其中子区域的划分方式有三种:一是将整幅单帧图像作为一个子区域,二是将单帧图像划分为数个大小相等的子区域,三是根据人体解剖学的知识,按照人身体各部分与身高的比例关系将单帧图像划分为数个非等子区域;然后提取每个子区域中的步态侧影或轮廓的形状特征并计算序列中步态形状的变化特征,从而构成描述步态序列的特征向量;最后的实验结果表明基于多区域形状特征的步态识别方法是一种行之有效的识别方法。
(2)提出了基于Radon变换的步态识别方法。Radon变换是一种计算图像在某一指定角度方向上投影的图像变换方法。人的步态运动过程中,胳膊、腿等部位的摆动角度都是在变化的,因此Radon变换可以用来表示人体步态的角度特征。基于Radon变换的步态识别方法对图像序列中的每一个步态周期构造一个Radon变换模板,并对模板提取步态特征。实验结果表明该方法可以取得较好的识别结果。
(3)提出了基于遗传算法的减法聚类方法和基于人的主要行走姿势的步态识别方法。基于遗传算法的减法聚类方法改进了传统的减法聚类,并利用遗传算法来优化参数,其聚类效果较传统减法聚类有明显的提高。基于人的主要行走姿势的步态识别方法首先使用基于遗传算法的减法聚类将每个序列中若干行走姿势分成指定个数的聚类,然后平均聚类中所有行走姿势以获得人的主要行走姿势,最后利用序列间主要行走姿势的匹配来实现步态识别。实验结果表明该方法的步态识别效果令人满意。
(4)提出了基于能量图的步态识别方法。由于图像相加法后平均,平均图像有较小的噪声,因此当利用图像加法或加减混合等运算应用于图像的平均来构造能量图时,能量图具有较小的噪声。基于能量图的步态识别方法为每个图像序列构造若干个能量图,并以这些能量图为基础提取步态特征、实现步态识别。本文中提出了两种能量图,即关键帧能量图和标准差能量图,而关键帧能量图又分为最大轮廓宽度关键帧能量图和最小轮廓宽度关键帧能量图,标准差能量图也有非零标准差能量图和零标准差能量图两种。实验结果表明基于能量图的步态识别方法具有较好的步态识别性能。
(5)提出了一种基于新的决策规则的球形支持向量机分类算法、一种基于核的模糊超球分类算法以及基于超球分类算法的步态识别方法。新的球形支持向量机使用新的决策规则可以取得比传统的球形支持向量机更高的模式分类正确率,而基于核的模糊超球分类算法不仅模式分类的正确率高于传统的球形支持向量机和移动中心超球分类算法,而且在运算时间的花费也比它们要少。基于超球分类算法的步态识别方法则是将上述两种超球分类算法分别应用于步态识别方法,以达到进一步地提高步态识别效果的目的。
With the development of modern society and the increase of people safety consciousness, human identity recognition is indispensable to the safety of more and more public places such as stations, airports, banks, administrations, communities and so on. Biometrics recognition, a kind of identity recognition technologies, can identify human identity according to physiology or activity characters and have the superiority over traditional recognition metods in accuracy and speed. Biometric features used currently include ear, hand geometry, fingerprint, palmprint, face, iris, voice, signature, etc. As a new technology of biometrics recognition, gait recognition has its predominance among other identity recognition technologies because it has the advantages of being noninvasive and requiring little about the quality of video. Now many researchers have paid growing attention to gait recognition. Based on its strong points and importance both in theoretical research and practical application, gait recognition will be further studied in this dissertation. The main contributions of this dissertation are summarized as follows:
(1) Proposed gait recognition methods based on shape features of multi-regions. Firstly, every human silhouette image is devided into several sub-regions in the video sequence. There are three kinds of sub-regions as follows: the whole silhouette image regarded as one sub-region, sub-regions with same sizes, sub-regions with different sizes that can be obtained according to properties of body segments extracted by using anatomical knowledge. Secondly, by means of extracting the silhouette or contour shape features of each sub-region and computing their changing features in the gait sequence, gait feature vectors can be constructed. Finally, experiments show that the proposed methods are valid and have good recognition results.
(2) Proposed a gait recognition method based Radon transform. The Radon transform belonging to image transform can measure the projections of image in certain directions. The Radon transform can represent gait angular features. The reason is that during human walking, there is large variation in the angles formed by leg and arm swinging. The proposed method constructs a Radon transform template for every gait cycle in the gait sequence and extracts gait features from this template. Experimental results show that the proposed method can gain better recognition performance.
(3) Proposed a subtractive clustering method based on genetic algorithms and a gait recognition approach based on main walking postures. The traditional subtractive clustering is modified and the genetic algorithms are employed to optimize the relative parameters in the improved subtractive clustering. Clustering experimental results show that the new clustering method can get the higher clustering accuracies than the traditional subtractive clustering. The new gait recognition approach firstly divides many walking postures of every sequence into several clusters by the subtractive clustering method based on genetic algorithms. Secondly, all walking postures from a cluster are averaged in order to gain one main walking posture. Finally, gait recognition is done by matching main walking postures among imge sequences. Gait recognition experimental results show that the new gait recognition approach can get the satisfying performance.
(4) Proposed gait recognition methods based on energy images. If the average image is calculated after several images are added, it has little noise. Because energy images are formed by applying image addition to image average, they have the advantage of little noise. The proposed method constructs several energy images for every gait sequence and extracts gait features from these images to do gait recognition. There are two sorts of energy images as follows: key frame energy images including key frame energy images with maximal contour width and key frame energy images with minimal contour width, standard deviation energy images including zero standard deviation energy images and non-zero standard deviation energy images. Experimental results show that the proposed methods can achieve encouraging gait recognition performance.
(5) Proposed a sphere-structured support vector machines classification algorithm based on a new decision rule, a novel kernel-based fuzzy hyperspheres classification algorithm, and gait methods based on hyperspheres classification algorithms. Firstly, because of using a new decision rule, the new sphere-structured support vector machines can gain the higher classification accuracies than traditional sphere-structured support vector machines. Secondly, the kernel-based fuzzy hyperspheres classification algorithm not only attain the better classification accuracies and lower computational complexity than traditional sphere-structured support vector machines and the hyperspheres algorithms based on moving median centers. Finally, two proposed hyperspheres classification algorithms metioned above are applied to relative gait recognition methods in order to further improve the recognition performance.
(1)提出了基于多区域形状特征的步态识别方法。该方法首先将图像序列中每帧步态侧影图像划分为若干子区域,其中子区域的划分方式有三种:一是将整幅单帧图像作为一个子区域,二是将单帧图像划分为数个大小相等的子区域,三是根据人体解剖学的知识,按照人身体各部分与身高的比例关系将单帧图像划分为数个非等子区域;然后提取每个子区域中的步态侧影或轮廓的形状特征并计算序列中步态形状的变化特征,从而构成描述步态序列的特征向量;最后的实验结果表明基于多区域形状特征的步态识别方法是一种行之有效的识别方法。
(2)提出了基于Radon变换的步态识别方法。Radon变换是一种计算图像在某一指定角度方向上投影的图像变换方法。人的步态运动过程中,胳膊、腿等部位的摆动角度都是在变化的,因此Radon变换可以用来表示人体步态的角度特征。基于Radon变换的步态识别方法对图像序列中的每一个步态周期构造一个Radon变换模板,并对模板提取步态特征。实验结果表明该方法可以取得较好的识别结果。
(3)提出了基于遗传算法的减法聚类方法和基于人的主要行走姿势的步态识别方法。基于遗传算法的减法聚类方法改进了传统的减法聚类,并利用遗传算法来优化参数,其聚类效果较传统减法聚类有明显的提高。基于人的主要行走姿势的步态识别方法首先使用基于遗传算法的减法聚类将每个序列中若干行走姿势分成指定个数的聚类,然后平均聚类中所有行走姿势以获得人的主要行走姿势,最后利用序列间主要行走姿势的匹配来实现步态识别。实验结果表明该方法的步态识别效果令人满意。
(4)提出了基于能量图的步态识别方法。由于图像相加法后平均,平均图像有较小的噪声,因此当利用图像加法或加减混合等运算应用于图像的平均来构造能量图时,能量图具有较小的噪声。基于能量图的步态识别方法为每个图像序列构造若干个能量图,并以这些能量图为基础提取步态特征、实现步态识别。本文中提出了两种能量图,即关键帧能量图和标准差能量图,而关键帧能量图又分为最大轮廓宽度关键帧能量图和最小轮廓宽度关键帧能量图,标准差能量图也有非零标准差能量图和零标准差能量图两种。实验结果表明基于能量图的步态识别方法具有较好的步态识别性能。
(5)提出了一种基于新的决策规则的球形支持向量机分类算法、一种基于核的模糊超球分类算法以及基于超球分类算法的步态识别方法。新的球形支持向量机使用新的决策规则可以取得比传统的球形支持向量机更高的模式分类正确率,而基于核的模糊超球分类算法不仅模式分类的正确率高于传统的球形支持向量机和移动中心超球分类算法,而且在运算时间的花费也比它们要少。基于超球分类算法的步态识别方法则是将上述两种超球分类算法分别应用于步态识别方法,以达到进一步地提高步态识别效果的目的。
With the development of modern society and the increase of people safety consciousness, human identity recognition is indispensable to the safety of more and more public places such as stations, airports, banks, administrations, communities and so on. Biometrics recognition, a kind of identity recognition technologies, can identify human identity according to physiology or activity characters and have the superiority over traditional recognition metods in accuracy and speed. Biometric features used currently include ear, hand geometry, fingerprint, palmprint, face, iris, voice, signature, etc. As a new technology of biometrics recognition, gait recognition has its predominance among other identity recognition technologies because it has the advantages of being noninvasive and requiring little about the quality of video. Now many researchers have paid growing attention to gait recognition. Based on its strong points and importance both in theoretical research and practical application, gait recognition will be further studied in this dissertation. The main contributions of this dissertation are summarized as follows:
(1) Proposed gait recognition methods based on shape features of multi-regions. Firstly, every human silhouette image is devided into several sub-regions in the video sequence. There are three kinds of sub-regions as follows: the whole silhouette image regarded as one sub-region, sub-regions with same sizes, sub-regions with different sizes that can be obtained according to properties of body segments extracted by using anatomical knowledge. Secondly, by means of extracting the silhouette or contour shape features of each sub-region and computing their changing features in the gait sequence, gait feature vectors can be constructed. Finally, experiments show that the proposed methods are valid and have good recognition results.
(2) Proposed a gait recognition method based Radon transform. The Radon transform belonging to image transform can measure the projections of image in certain directions. The Radon transform can represent gait angular features. The reason is that during human walking, there is large variation in the angles formed by leg and arm swinging. The proposed method constructs a Radon transform template for every gait cycle in the gait sequence and extracts gait features from this template. Experimental results show that the proposed method can gain better recognition performance.
(3) Proposed a subtractive clustering method based on genetic algorithms and a gait recognition approach based on main walking postures. The traditional subtractive clustering is modified and the genetic algorithms are employed to optimize the relative parameters in the improved subtractive clustering. Clustering experimental results show that the new clustering method can get the higher clustering accuracies than the traditional subtractive clustering. The new gait recognition approach firstly divides many walking postures of every sequence into several clusters by the subtractive clustering method based on genetic algorithms. Secondly, all walking postures from a cluster are averaged in order to gain one main walking posture. Finally, gait recognition is done by matching main walking postures among imge sequences. Gait recognition experimental results show that the new gait recognition approach can get the satisfying performance.
(4) Proposed gait recognition methods based on energy images. If the average image is calculated after several images are added, it has little noise. Because energy images are formed by applying image addition to image average, they have the advantage of little noise. The proposed method constructs several energy images for every gait sequence and extracts gait features from these images to do gait recognition. There are two sorts of energy images as follows: key frame energy images including key frame energy images with maximal contour width and key frame energy images with minimal contour width, standard deviation energy images including zero standard deviation energy images and non-zero standard deviation energy images. Experimental results show that the proposed methods can achieve encouraging gait recognition performance.
(5) Proposed a sphere-structured support vector machines classification algorithm based on a new decision rule, a novel kernel-based fuzzy hyperspheres classification algorithm, and gait methods based on hyperspheres classification algorithms. Firstly, because of using a new decision rule, the new sphere-structured support vector machines can gain the higher classification accuracies than traditional sphere-structured support vector machines. Secondly, the kernel-based fuzzy hyperspheres classification algorithm not only attain the better classification accuracies and lower computational complexity than traditional sphere-structured support vector machines and the hyperspheres algorithms based on moving median centers. Finally, two proposed hyperspheres classification algorithms metioned above are applied to relative gait recognition methods in order to further improve the recognition performance.
引文
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