CIF: Small: Collaborative Research: Geometry-aware and data-adaptive signal processing for resource constrained activity analysis

CIF：小型：协作研究：用于资源受限活动分析的几何感知和数据自适应信号处理

• 批准号: 1319658
• 负责人: Anuj Srivastava
• 金额: $ 17.5万
• 依托单位: Florida State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1319658&HistoricalAwards=false
• 关键词: CIF; Small; Collaborative; Research; Geometry

The task of characterizing human activities using multimodal sensing and automated inference techniques is critically important in many applications. Human activities when observed using traditional audio and video sensors, as well as novel emerging sensors such as depth cameras, orientation sensors, and smart personal devices, result in complex, high-dimensional spatiotemporal signatures that are difficult to analyze. The sources of difficulty are many: high data throughput, the need for sensor registration, variable execution rates associated with activities, pose variability in sensing, the non-Euclidean nature of feature spaces due to physical constraints on environments and actors, and data corruption due to occlusions. The current techniques, involving Euclidean representations and multivariate analyses, fall short in characterizing human activities, as they do not handle non-Euclidean structures nor obtain invariances to pose and execution rates. This research uses tools spanning differential geometry, statistics and signal approximation theory to develop novel frameworks for characterizing human activities. These fundamental tools lead to comprehensive solutions that are applicable to a broad swath of traditional and emerging sensors. The salient aspects of this approach are: 1) geometry awareness, encompassing both classical Euclidean as well as non-Euclidean feature spaces, 2) invariance to sensor placement and execution rate tightly integrated into the representation, and 3) data adaptivity leading to low bitrate representation of human activities for reduced communication and low computational scenarios. Applications of this research include monitoring of human activities using off-the-shelf sensors in resource-constrained environments, such as at homes and on mobile devices.

使用多模态传感和自动推理技术表征人类活动的任务在许多应用中至关重要。当使用传统的音频和视频传感器以及新型新兴传感器（例如深度相机、方向传感器和智能个人设备）观察人类活动时，会产生难以分析的复杂的高维时空签名。困难的来源有很多：高数据吞吐量，传感器注册的需要，与活动相关的可变执行速率，在感测中的姿势变化，由于环境和演员的物理约束，以及由于遮挡的数据损坏的特征空间的非欧几里德性质。目前的技术，涉及欧几里德表示和多元分析，在表征人类活动，因为它们不处理非欧几里德结构，也没有获得不变性的姿势和执行率，不足。这项研究使用微分几何，统计学和信号近似理论的工具来开发表征人类活动的新框架。这些基本工具可提供适用于广泛的传统和新兴传感器的全面解决方案。这种方法的突出方面是：1）几何感知，包括经典的欧几里德以及非欧几里德特征空间，2）传感器放置和执行速率的不变性紧密集成到表示中，以及3）数据自适应性导致人类活动的低比特率表示，用于减少通信和低计算场景。这项研究的应用包括在资源受限的环境中使用现成的传感器监测人类活动，例如在家中和移动的设备上。