A Systems Theoretic Approach to Robust Active Vision

主动视觉-控制和计算机视觉的融合-定位在一个最佳的情况下，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，能够发现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错误，这些应用就不可能实现，例如，由于校准不良的相机，模糊或图像之间仅部分确定的特征对应。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家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，在计算机视觉和控制社区中都有一个共识，尽管视觉控制具有潜在的力量，但这些技术在非结构化环境中成功应用的实例相对较少。这在很大程度上是可以追溯的；采用经典方法设计的主动视觉系统存在盗窃敏捷性（即鲁棒性不足）问题。本提案的动机是co - pi的初步工作，强烈表明这种脆弱性可以通过诉诸共同的系统理论基础来解决，从而使问题的不同方面之间的相互联系更强、更直接。具体来说，提出的研究目标是：开发一个系统设计可证明的鲁棒主动视觉系统的范例。这个范例将在一个共同的系统理论框架内解决问题的计算机视觉和控制方面，利用它们的协同作用来优化性能。该方法提供的优点包括：(i)将基于鲁棒识别/模型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）结合计算机视觉和动态效应以及相应的不确定性结构的模型的稳健识别。（iii）利用这些模型和相关不确定性结构来稳健优化性能的控制器设计。综合实验验证和性能表征的结果系统，使用几个平移和倾斜单元和专用的图像处理硬件，目前在我们的实验室可用。本研究的重点是认识到在一系列帧中鲁棒跟踪目标的问题和鲁棒性能分析有一个共同的事实：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略有不同，但这允许开发一组通用工具，通过利用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.