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取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，来自校准不良的相机，模糊或仅部分确定的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起来。然而，在计算机视觉和控制社区中有一个共识，即尽管视觉控制的隐含力量，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盗窃敏捷性（即缺乏鲁棒性）。本建议的动机是共同方案执行机构的初步工作，这些工作强烈表明，这种脆弱性可以通过诉诸一个共同系统理论基础来解决，使问题的不同方面之间的相互联系更强、更直接。具体而言，所提出的研究的目标是：开发一个范例，系统地设计可证明的强大的主动视觉系统。这种模式将在一个共同的系统理论框架内解决计算机视觉和控制方面的问题，利用它们的协同作用来优化性能。由该方法提供的优点的示例包括：（i）将基于鲁棒识别/模型（输入）验证的预测集成到目标定位算法中，以提高鲁棒性并减少搜索时间。(ii)结合联合收割机计算机视觉和动态效应的模型的鲁棒识别，以及相应的不确定性结构。(iii)控制器设计，利用这些模型和相关的不确定性结构鲁棒优化性能。使用我们实验室目前可用的几个平移和倾斜单元以及专用图像处理硬件，对所产生的系统进行全面的实验验证和性能表征。研究的关键是认识到帧序列中目标的鲁棒跟踪问题和鲁棒性能分析问题有一个共同点：它们等价于分析满足一定插值条件的有界L2到L2算子的存在性。虽然每种情况下的细节都有些不同，但这允许开发一套通用的工具，通过吸引凸分析的丰富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.