A Systems Theoretic Approach to Robust Active Vision

主动视觉-控制和计算机视觉的融合-处于最佳状态，以满足日益增长的人口部分的需求。有意识的环境将使老年人能够独立生活。通过解读面部表情来获得用户困惑或沮丧的线索的计算机可以带来更简单的界面。最后，能够检测可疑活动的智能活动监视系统可以大大提高防止悲剧发生的能力。显然，如果不使用反馈来补偿不确定性和误差，这些应用将是不可能的，例如，源于校准不佳的相机、模糊或仅部分确定图像之间的特征对应。事实上，计算机视觉和控制已经通过包括宾夕法尼亚州立大学在内的几个研究机构开发的许多成功的概念验证系统联系在一起。然而，计算机视觉和控制界都有一个共识，即尽管视觉控制具有隐含的力量，但这些技术在非结构化环境中成功应用的实例相对较少。这在很大程度上可以追溯到使用经典方法设计的主动视觉系统的偷窃敏捷性(即缺乏健壮性)。本提议的动机是联合绩效指标所做的初步工作，有力地表明，这一脆弱性可以通过呼吁共同系统理论基础使问题不同方面之间的相互联系更有力和更直接地得到解决。具体地说，拟议研究的目标是：开发一种系统地设计可证明稳健的主动视觉系统的范例。这一范例将在一个共同的系统理论框架内解决计算机视觉和问题控制方面的问题，利用它们的协同作用来优化性能。这种方法提供的优点的例子包括：(I)将基于稳健识别/模型(IN)验证的预测集成到目标定位算法中，以提高稳健性并减少搜索时间。(2)稳健地识别结合了计算机视觉和动力效应的模型，以及相应的不确定性结构。(Iii)控制器设计，它利用这些模型和相关的不确定性结构来强有力地优化性能。使用我们实验室目前提供的几个平移和倾斜装置和专用图像处理硬件，对所产生的系统进行全面的实验验证和性能表征。研究的重点是认识到，在一系列帧中稳健跟踪目标和稳健性能分析的问题都有一个基本的共同事实：它们等价于分析满足一定内插条件的有界L2到L2算子的存在性。虽然每种情况的细节略有不同，但这允许开发一组通用的工具，方法是求助于丰富的凸分析语言，将这些问题重新转换为LMI优化形式。

Active vision - the confluence of control and computer vision - is positioned in an optimal situation to address the needs of a growing segment of the population. Aware environments would enable elderly people to carry on independent lives. Computers that interpret facial expressions to obtain cues to user confusion or frustration can lead to simpler interfaces. Finally, intelligent activity surveillance systems capable of detecting suspicious activities can substantially improve the ability to prevent tragedies. Clearly these applications would not be possible without the use of feedback to compensate for uncertainty and errors, stemming for instance, from poorly calibrated cameras, blurring or only partially determined feature correspondences between images. Indeed, computer vision and control are already linked through many successful proof-of-concept systems developed at several research institutions, including Penn State. However, there is a consensus in both the computer vision and control communities that, in spite of the implicit power of visual control, there are relatively few instances where these techniques have been successfully applied in unstructured environments. This can be traced, to a large extent; to theft-agility (i.e. lack of robustness) of active vision systems designed using classical methods. The present proposal is motivated by preliminary work by the Co-PIs strongly indicating that this fragility can be addressed by appealing to a common systems theoretic substrate to make the interconnection between the different aspects of the problem stronger and more direct. Specifically, the objectives of the proposed research are: Development of a paradigm for systematically designing provably robust active vision systems. This paradigm will address the computer vision and control aspects of the problem within a common systems-theoretic framework, exploiting their synergism to optimize performance. Examples of the advantages offered by this approach include: (i) Integration of robust identification/model (in)validation based predictions into target localization algorithms to improve robustness and reduce search time. (ii) Robust identification of models that combine computer vision and dynamical effects, as well as the corresponding uncertainty structure. (iii) Controller design that exploits these models and the associated uncertainty structure to robustly optimize performance. Comprehensive experimental validation and performance characterization of the resulting systems, using several pan and tilt units and dedicated image processing hardware currently available in our lab. The key point of the research is the realization that the problems of robustly tracking an object in a sequence of frames and robust performance analysis, share an underlying common fact: they are equivalent to analyzing the existence of a bounded L2 to L2 operator that satisfies certain interpolation conditions. While the details are somewhat different in each case, this allows for developing a common set of tools, by appealing to the rich language of convex analysis to recast these problems into an LMI optimization form.