Visual Models - Application to Situational Awareness

视觉模型 - 态势感知的应用

• 批准号: RGPIN-2016-04638
• 负责人: Ferrie, Frank
• 金额: $ 1.6万
• 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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=708431
• 关键词: Visual; Models; Application; Situational; Awareness

My research addresses the problem of representations for computer vision, specifically how to capture information from a sensor network and create computer models that faithfully reflect an environment over time. There are 3 specific sub-projects within this proposal. The first deals with providing situational awareness for a robotic assistant working in conjunction with a human. Objects (humans + machines) are represented as articulated 3D models that track their real world counterparts in real time. The scientific focus is how to capture and model dynamic behavior so that the system can identify or control the activities of each occupant. Following on previous work with GM research, the goal is to embed robotic assistants with more powerful and reliable situational awareness so that practical implementations become possible. If this vision is successful, future assembly lines will be comprised of human-robot teams collaborating to manufacture products, greatly scaling up productivity and allowing Canada to remain competitive in the global marketplace. The remaining 2 sub-projects are aimed at representations that enable humans to have more precise knowledge about their environments, and robots to be able to function within them. In the first, Sparse Data Models, we look at the problem of how to recover descriptions of an environment with limited sensory ability. The specific example is localizing underground mineral deposits from physical core samples, which are very sparse relative to what one finds in images. We recently have developed some new stochastic modeling techniques that show promise for extending conventional image reconstruction algorithms to these challenging datasets. If successful, this research will lead to more precise algorithms for localizing mineral deposits, which in turn could have a significant impact on mining operations by reducing costs for excavation, transport and processing. Finally, the Deep Learning sub-project is an attempt to leverage impressive technical progress in machine learning to determine representations that are better suited to natural forms. Our focus is on the practical implementation of Hierarchical Generative Models using Convolutional Deep Boltzman machine networks, with the goal of achieving comparable performance with a substantial reduction in complexity. This research will contribute to work with GM in building sensor systems that can operate in off-road environments under adverse weather conditions, as well as resource industry projects that involve identifying structures and landmarks in GPS-deprived environments.

我的研究解决了计算机视觉的表示问题，特别是如何从传感器网络捕获信息并创建忠实地反映环境随着时间的时候的计算机模型。 该提案中有3个特定的子项目。 第一个涉及为与人一起工作的机器人助手提供情境意识。 物体（人类 +机器）表示为铰接的3D模型，可实时跟踪其现实世界中的对应物。 科学重点是如何捕获和建模动态行为，以便系统可以识别或控制每个乘员的活动。 在以前的GM研究工作之后，目标是将机器人助手嵌入更强大，更可靠的情境意识，以便实现实际实施。 如果这种愿景成功，未来的装配线将由人机团队组成，这些团队合作生产产品，大大扩大生产率并允许加拿大在全球市场上保持竞争力。 其余的两个子项目的目的是使人能够对环境具有更精确的知识，并能够在其内部运作。 在第一个稀疏数据模型中，我们研究了如何以有限的感官能力恢复对环境的描述的问题。 具体的例子是从物理核心样品中定位地下矿物质沉积物，相对于图像中发现的情况非常稀疏。 我们最近开发了一些新的随机建模技术，这些技术显示了将常规图像重建算法扩展到这些具有挑战性的数据集的希望。 如果成功的话，这项研究将导致更精确的算法来定位矿藏，这反过来又可能通过降低开挖，运输和加工成本来对采矿业务产生重大影响。 最后，深度学习子项目是试图利用机器学习中令人印象深刻的技术进步来确定更适合自然形式的表示形式。 我们的重点是使用卷积Deep Boltzman机器网络对层次生成模型的实际实施，目的是实现可比的性能，并大大降低复杂性。 这项研究将有助于与通用汽车合作，以在不利天气条件下在越野环境中运行的建筑传感器系统，以及涉及识别GPS剥夺环境中的结构和地标的资源行业项目。