CASIA OpenIR  > 毕业生  > 博士学位论文
多视角步态分析与识别
其他题名Multi-view Gait Analysis and Recognition
于仕琪
学位类型工学博士
导师谭铁牛
2007-11-25
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 步态分析 多视角 视觉监控 性别识别 Biometrics Gait Recognition Gait Analysis Multi-view Gait Feature Visual Surveillance Gender Classification
摘要由于严峻的公共安全形势,智能视觉监控越来越受到重视。步态识别能够从远距离识别出人的身份,对提高监控系统的智能性至关重要。步态识别的难点之一是步态特征作为一种行为特征,具有很强的不稳定性。特别是在视角变化、衣着变化、携带物品、光照变化、时间变化等情况下,提取稳定的具有区分能力的特征变得非常困难。本文针对步态识别中的视角变化这一常见变化,进行了一系列研究,并对衣着和携带物品变化等因素也有所涉及。此外,还对基于步态的性别识别这一新方向进行了探索。 本文的主要工作有: 1. 目前步态识别研究尚不成熟,步态识别缺乏统一评价标准,而且有很多问题尚没有研究清楚。针对这一现状,创建了一个步态识别算法评价框架。这个评价框架包括一个大规模的步态数据库(CASIA~步态数据库数据集~B),三组实验和一系列评价指标。该框架可以评价某个步态识别算法对视角变化,衣着变化和携带物品变化的稳定性,指导步态算法的设计。 2. 哪个视角最适合用于步态识别?视角变化是如何影响识别率的?这两个问题是步态识别研究中的两个重要问题。本文对视角变化与步态识别性能的关系进行了建模和分析,分析得出侧面视角是步态识别中的最佳视角的结论;另外,还建立起视角与识别率之间的关系模型。这些对步态识别研究具有一定的指导意义。 3. 目前虽然有大量的步态识别算法被提出,但是这些算法大部分都是基于特定视角的,对视角变化不鲁棒,这极大的限制了步态识别的应用。文中分别提出了一种线性和一种非线性的模型,将一个视角的步态数据合成为该人在另一个视角的数据,来解决测试数据的视角跟注册数据的视角不一致问题。大量的实验验证了该算法对于解决视角变化问题非常有效。 4. 已有的研究证明可以根据步态区分性别。性别可以用于步态特征的粗分类,用于提高步态数据库的检索速度和提高步态识别的准确率。另外,性别识别还可以提高监控系统的感知能力,对进入场景中的行人进行信息搜集。在文中使用了多种特征和多种分类器从多个视角进行性别识别研究,并比较了各种特征以及分类器性能,在实验中发现了一些有用的结论,如身体的哪些部分最能表现人的性别,哪些特征最有区分能力等。针对步态特征常见的视角变化问题,提出了解决方法,获得了令人鼓舞的结果。除此之外,还进行了跨人种的性别识别,获得了比较高的识别率,证明了不同人种的相同性别的人的步态是相似的。
其他摘要With the increasing requirements for security, intelligent visual surveillance gained more and more attentions. Gait can be used as a kind of biometric to identify human at a distance. Gait recognition is a key element for improving the intelligence of the surveillance systems. Compared with other biometric features, the shortcoming of gait is that it is not robust to variations, such as view, clothing, carrying condition, illumination. View variation is very common in visual surveillance. In the dissertation we investigate view variation in gait analysis and recognition. Some other variations, clothing and carrying condition, are also considered. Besides, some research work is also done on gait based gender classification. This dissertation mainly includes the following issues: 1. Gait recognition is in its immaturity, and there is still no standard to evaluate different algorithms. We propose a evaluation framework to do this work and advance gait recognition technology. The framework contains a large gait database (Data Set B in CASIA Gait Database), 3 sets of experiments and some metrics. The framework can evaluate an algorithm's robustness to view, clothing and carrying condition variations. 2. There are two remaining open problems in gait recognition. One is which view is the most suitable one for gait recognition and why it is. Another is how view angle variation affects the performance of gait recognition. We proposes two models, a geometrical one and a mathematical one, in an attempt to address these two questions, and investigate and analyze the effect of view angle on the performance of appearance-based gait recognition. 3. Currently most gait recognition algorithms are view dependent and not robust to view variation. We proposed a linear model and a non-linear model to synthesize the gait feature from one view to another view. The models can be used when the probe angle is not equal to the gallery angle. Experimental results show that the models can solve view variation problem. 4. Some previous research shows that gait feature can be used for gender classification. Gait feature can be divided into two groups by gender, male and female, so gender can help to speed up gait database retrieval, improve gait recognition accuracy and surveillance systems' perception. We give a comprehensive study on gait based gender classification, which includes experiments by human observers and computer algorithms, comparison of different gait features and classifiers, experiments from multi-view and view invariant gender classification. We also do cross-race gender classification experiments and gained inspiring results.
馆藏号XWLW1158
其他标识符200418014628048
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/6038
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
于仕琪. 多视角步态分析与识别[D]. 中国科学院自动化研究所. 中国科学院研究生院,2007.
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