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: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=615203
• 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随着时间的推移忠实反映环境的计算机模型。该提案中有 3 个具体子项目。第一个涉及为与人类合作的机器人助手提供态势感知。对象（人类 + 机器）被表示为铰接式 3D 模型，可以实时跟踪现实世界中的对象。科学的重点是如何捕捉和建模动态行为，以便系统能够识别或控制每个乘员的活动。继之前与通用汽车研究的合作之后，目标是嵌入具有更强大和可靠的态势感知功能的机器人助手，以便实际实施成为可能。如果这一愿景成功，未来的装配线将由人机团队组成，协作制造产品，从而大大提高生产率，并使加拿大在全球市场上保持竞争力。 剩下的 2 个子项目旨在使人类能够更准确地了解其环境，并使机器人能够在其中发挥作用。在第一个稀疏数据模型中，我们研究如何恢复感知能力有限的环境的描述的问题。具体的例子是从物理岩心样本中定位地下矿藏，这些样本相对于图像中发现的情况来说非常稀疏。我们最近开发了一些新的随机建模技术，这些技术有望将传统的图像重建算法扩展到这些具有挑战性的数据集。如果成功，这项研究将带来更精确的矿藏定位算法，从而降低挖掘、运输和加工成本，从而对采矿作业产生重大影响。 最后，深度学习子项目试图利用机器学习领域令人印象深刻的技术进步来确定更适合自然形式的表示。我们的重点是使用卷积深度玻尔兹曼机器网络来实际实现分层生成模型，目标是在大幅降低复杂性的情况下实现可比的性能。这项研究将有助于与通用汽车合作构建可在恶劣天气条件下的越野环境中运行的传感器系统，以及涉及在缺乏 GPS 的环境中识别结构和地标的资源行业项目。