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Tensorial modeling of dynamical systems for gait and activity recognition

用于步态和活动识别的动态系统张量建模

基本信息

批准号：
EP/I018719/1
负责人：
Fabio Cuzzolin
金额：
$ 12.53万
依托单位：
Oxford Brookes University
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2011
资助国家：
英国
起止时间：
2011 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FI018719%2F1
关键词：
Tensorial modeling dynamical systems gait

项目摘要

Biometrics such as face, iris, or fingerprint recognition have received growing attention in the last decade, as automatic identification systems for surveillance and security have started to enjoy widespread diffusion. They suffer, however, from two major limitations: they cannot be used at a distance, and require user cooperation, assumptions impractical in real-world scenarios. Interestingly, psychological studies show that people are capable of recognizing their friends just from the way they walk, even when their gait is poorly represented by point light display. Gait has several advantages over other biometrics, as it can be measured at a distance, is difficult to disguise or occlude, can be identified even in low-resolution images, and is non-cooperative in nature. Furthermore, gait and face biometrics can be easily integrated for human identity recognition.Despite its attractive features, though, gait identification is still far from being ready to be deployed in practice. What limits its adoption in real-world scenarios is the influence of a large number of nuisance factors which affect appearance and dynamics of the gait. These include, for instance: walking surface, lighting, camera setup (viewpoint), but also footwear and clothing, objects carried, time of execution, walking speed. Similar issues are shared by other applications of motion classification, such as action and activity recognition. Multilinear or tensorial models, in which a number of (nuisance) factors linearly mix to generate what we observe (in our case the walking gait), have been proven in the recent past to be able to describe the influence of such factors, for instance in the context of face recognition. However, video sequences are more complex objects than single images. We first need to represent video footages in a compact way.Encoding the dynamics of videos by means of some sort of dynamical model has been proven effective in both action recognition and gait identification, in situations in which the dynamics is critically discriminative. Besides, the actions of interest have to be temporally segmented from a video sequence, while actions of sometimes very different lengths might have to be compared. Dynamical representations are very effective in coping with temporal detection and compression, and indeed several researchers have explored the idea of encoding motions via linear, nonlinear, stochastic or chaotic dynamical systems.In this project, therefore, we propose to develop a novel, general framework for the classification of video sequences (with a focus on the walking gait), based on the application of tensorial decomposition techniques to image sequences represented as realizations of suitable dynamical models.The proposed framework will allow us to deal with the issue of the nuisance factors which greatly affect identification from gait and activity recognition in a principled way. The main goal is to push towards a more widespread diffusion of gait ID, as a concrete contribution to enhancing the security levels in the country in the current, uncertain scenarios. With their implications for crime prevention and security, biometrics and surveillance are fast growing business areas, a fact reflected by the increasing number of government-sponsored initiatives in the area in most advanced economies. In addition, the techniques devised in this proposal are extendable to action and identity recognition with immense commercial exploitation potential, ranging from content-based video retrieval from repositories such as YouTube, to HMI, to interactive video games, etcetera.

随着用于监控和安全的自动识别系统开始广泛普及，面部、虹膜或指纹识别等生物识别技术在过去十年中受到越来越多的关注。然而，它们有两个主要限制：它们不能远距离使用，并且需要用户合作，这些假设在现实场景中不切实际。有趣的是，心理学研究表明，即使点光源显示无法很好地体现他们的步态，人们也能够仅通过他们走路的方式来识别他们的朋友。与其他生物识别技术相比，步态有几个优点，因为它可以远距离测量，难以伪装或遮挡，即使在低分辨率图像中也可以识别，并且本质上是非合作的。此外，步态和面部生物识别技术可以轻松集成以进行人类身份识别。尽管步态识别具有吸引人的功能，但它还远未准备好在实践中部署。限制其在现实场景中采用的是大量影响步态外观和动态的干扰因素的影响。例如，这些包括：行走表面、照明、摄像机设置（视点），还包括鞋类和服装、携带的物品、执行时间、行走速度。运动分类的其他应用也存在类似的问题，例如动作和活动识别。多线性或张量模型，其中许多（讨厌的）因素线性混合以生成我们所观察到的（在我们的例子中是步行步态），最近已被证明能够描述这些因素的影响，例如在人脸识别的背景下。然而，视频序列是比单个图像更复杂的对象。我们首先需要以紧凑的方式表示视频片段。在动态具有严格判别性的情况下，通过某种动态模型对视频动态进行编码已被证明在动作识别和步态识别中都是有效的。此外，感兴趣的动作必须在时间上从视频序列中分段，而有时可能必须比较长度非常不同的动作。动态表示在处理时间检测和压缩方面非常有效，实际上，一些研究人员已经探索了通过线性、非线性、随机或混沌动态系统对运动进行编码的想法。因此，在这个项目中，我们建议开发一种新颖的、通用的视频序列分类框架（重点关注步行步态），基于张量分解技术对表示为适当动态实现的图像序列的应用。所提出的框架将使我们能够以原则性的方式处理极大影响步态和活动识别识别的干扰因素问题。主要目标是推动步态 ID 的更广泛传播，为在当前不确定的情况下提高该国的安全水平做出具体贡献。由于生物识别和监控对犯罪预防和安全的影响，生物识别和监控成为快速增长的业务领域，大多数发达经济体中越来越多的政府资助该领域的举措就反映了这一事实。此外，该提案中设计的技术可扩展到具有巨大商业开发潜力的动作和身份识别，范围从基于内容的视频检索（从 YouTube 等存储库到 HMI，再到交互式视频游戏等）。