Interpretation of visual models

视觉模型解读

• 批准号: 36560-2011
• 负责人: Ferrie, Frank
• 金额: $ 1.46万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=568750
• 关键词: Interpretation; visual; models

We define a visual model as a representation of the 3D shape, structure, behaviour, etc., of complex objects or environments, derived from information obtained from sensors that reflect visual information. Like much of the research in computer vision over the years, our work has focused on how such models are computed from sensory data, often with the goal of determining a larger attribute or state. In recent years we have focused on the specific problem of modeling urban environments using information captured by mobile sensors and surveillance systems. The current proposal focuses on several aspects of this research that have become increasingly important in the field, as well as applications of this research that are of interest to our industrial partners. Of particular interest is the problem of situational awareness - the ability of a system to intentionally gather information from multiple sensor streams, fuse it into a consistent representation of the environment, and respond to queries about state in an asynchronous fashion. We will investigate two specific instances - monitoring changes to and activities within a large scale urban environment and ensuring worker health and safety on the factory floor. This will involve performing the research necessary to advance the state of the art in modeling complex environments, building more powerful tools for accessing temporal variations in the data, devising the means to control sensor networks and fuse information, and finally to develop computer representations sufficiently powerful to host these models. As in the past, we will work with industrial partners to provide grounding for the research, experimental validation, and technology transfer. In the long term we hope to develop algorithms that can not only infer models of complex environments, but also simulate them as well. Being able to make such predictions could dramatically alter the way in which we manage and remediate our urban infrastructure.

我们将视觉模型定义为复杂对象或环境的 3D 形状、结构、行为等的表示，源自从反映视觉信息的传感器获得的信息。 与多年来计算机视觉领域的许多研究一样，我们的工作重点是如何根据传感数据计算此类模型，通常目标是确定更大的属性或状态。 近年来，我们专注于利用移动传感器和监控系统捕获的信息对城市环境进行建模的具体问题。 当前的提案重点关注该研究在该领域变得越来越重要的几个方面，以及我们的工业合作伙伴感兴趣的该研究的应用。 特别令人感兴趣的是态势感知问题——系统有意识地从多个传感器流收集信息，将其融合成环境的一致表示，并以异步方式响应有关状态的查询的能力。 我们将调查两个具体实例——监测大规模城市环境的变化和活动，以及确保工厂车间工人的健康和安全。 这将涉及进行必要的研究，以推进复杂环境建模的最先进水平，构建更强大的工具来访问数据的时间变化，设计控制传感器网络和融合信息的方法，最后开发足够强大的计算机表示托管这些模型。 与过去一样，我们将与工业合作伙伴合作，为研究、实验验证和技术转让提供基础。 从长远来看，我们希望开发出不仅可以推断复杂环境模型，还可以对其进行模拟的算法。 能够做出这样的预测可能会极大地改变我们管理和修复城市基础设施的方式。