CASIA OpenIR  > 毕业生  > 博士学位论文
步态分析与识别
其他题名Analysis and Recognition of Gait
王亮
学位类型工学博士
导师谭铁牛
2003-12-01
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业模式识别与智能系统
关键词生物特征识别 步态识别 视觉监控 人的运动分析 基于模型的跟踪 Biometrics Gait Recognition Visual Surveillance Human Motion Analysis And Model-based Tracking
摘要生物特征识别是一种利用人的生理或行为特征进行身份识别的技术。随着机场、银 行、军事基地等安全敏感场合对大范围视觉监控系统的迫切需求,远距离的身份识别研究 近来引起了计算机视觉研究者们的浓厚兴趣。脸像、指纹、虹膜等牛物特征,通常要求近 距离或者接触性的感知,而步态是远距离情况下唯一可感知的生物特征。因此从视觉监控 的观点来看,步态是非常有潜力的生物特征。 步态识别是个新兴的研究领域,它利用人走路的姿势进行身份识别。围绕这个主题, 本文开展了如下几方面的研究工作: ①基于“从行走运动的时空模式中可学习人体的外观模型”的观点,提出了一种基于 统计形状分析的步态识别算法。对于每个序列而言,背景减除过程用来提取行人的 运动轮廓;这些轮廓随时间的姿态变化在二维空间中被对应描述为一个序列的复数 配置(Complex Configuration):利用Procrustes形状分析方法从该序列配置中获取 主轮廓模型作为人体的静态外观特征。实验结果表明该算法获得了令人鼓舞的识别 性能。 ②基于“人体行走运动很大程度上依赖于轮廓随着时间的形状变化”的直观想法,提 出了一种基于时空轮廓分析的步态识别算法。对于每1序列而言,背景减除与轮廓 相关方法用于检测和跟踪行人的运动轮廓;这些时变的二维轮廓形状被转换为对应 的一维距离信号,同时通过特征空间变换来提取低维步态特征;基于时空相关或归 一化欧氏距离度量,标准的模式分类技术用于最终的识别。实验结果表明该算法不 仅获得了令人满意的识别性能,而且拥有相对较低的计算代价。 ③基于“行走运动的关节角度变化包含着丰富的个体识别信息”的思想,提出了一种 基于模型的步态识别算法。首先,结合人体模型、运动模型和运动约束等先验知 识,利用Condensation算法进行行人的跟踪。然后,从跟踪结果中获取人体主要关 节的角度变化轨迹。这些轨迹经过结构和时间归一化后作为动态特征用于身份识 别。 ④基于“人体生物特征不仅包含静态外观信息,也包含行走运动的动态信息”的思 想,提出了一种判决级上融合人体静态和动态特征的身份识别方法。在不同融合规 则下的实验结果表明:融合后的识别性能均优于使用任何单一模态下的识别性能。
其他摘要Biometrics makes use of the physiological or behavioral characteristics of people to authenticate their identities. With a growing need for a full range of visual surveillance and monitoring systems in security-sensitive environments such as banks and airports, human identification at a distance has recently gained increasing interest from computer vision researchers.To operate successfully, the established biometrics such as face, fingerprint and iris usually require proximal sensing or physical contact. However, they are hardly applicable at a distance. Fortunately, gait, the way people walk, is still visible and can be easily perceived unobtrusively. So, from a surveillance perspective, gait is a very attractive modality. Gait recognition is a relatively new research direction. It aims to seek distinguishable variations between the same actions of walking from different people for the purpose of automatic identity verification. Focusing on this topic, this dissertation mainly includes the following issues: ① Based on the idea that a specific appearance model can be learned from spatial-temporal motion pattern of gait, we propose a simple and efficient gait recognition algorithm using statistical shape analysis. For each image sequence, an improved background subtraction procedure is used to extract moving silhouettes of the walker from the background. Temporal changes of thc detected silhouettes are then represented as an associated sequence of complex vector configurations in a common coordinate frame, and are further analyzed using the Procrustes shape analysis method to obtain an eigen-shape as signatures. This method does not directly analyze the dynamics of gait, but implicitly uses the action of walking to capture the structural characteristics of gait, especially the biometric shape cues. Experimental results demonstrate that the proposed algorithm has an encouraging recognition performance. ② Based upon an intuitive consideration that recognizing people by gait depends greatly on how the silhouette of human body changes over time, we present a non-parametric gait recognition method using PCA (Principal Component Analysis) . 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 the PCA is applied to 1D time-varying distance signals derived from a sequence of silhouette images to reduce the dimensionality of the input feature space. Supervised pattern classification tech- niques are finally performed in the lower-dimensional eigenspace for recognition. This method implicitly captures the structural and transitional characteristics of gait. Extensive experimental
馆藏号XWLW850
其他标识符850
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/5791
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
王亮. 步态分析与识别[D]. 中国科学院自动化研究所. 中国科学院研究生院,2003.
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