RI: Small: Intelligent Compressive Multi-Walker Recognition and Tracking (iSMART) through Pyroelectric Sensor Networks

RI：小型：通过热释电传感器网络进行智能压缩多步行者识别和跟踪 (iSMART)

• 批准号: 0915862
• 负责人: Qi Hao
• 金额: $ 32.59万
• 依托单位: University of Alabama Tuscaloosa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0915862&HistoricalAwards=false
• 关键词: RI; Small; Intelligent; Compressive; Multi

Although high-cost, data-intensive multi-camera systems have been widely used for mobile human tracking and recognition, the pyroelectric infrared (PIR) sensor has a variety of advantages including dramatically low costs, chemical stability, high sensitivity to human body thermal variation, and extremely low sensory data throughput.This project implements an Intelligent Compressive Multi-Walker Recognition and Tracking (iSMART) testbed based on PIR Sensor Networks (PSN). The novelties of iSMART include three aspects: (1) Context-aware region-of-interest (RoI) exploration to achieve an inherent tradeoff between area of sensor coverage and degree of information acquisition resolution. This research uses strict mathematical models to measure RoI context. (2) Decentralized inference / learning for in-network intelligence. This project develops a belief-propagation-based distributed inference scheme with data-to-object association for continuous tracking and recognition of multiple walkers. It uses orthogonal-projection-based distributed learning for sensor calibration and feature model training. (3) Networked, compressive sampling structures and sensing protocols. This project extends the latest progress in compressive and multiplex sensing theories to guide the design of novel networked sensor receiver pattern geometries and decentralized sensing protocols.The above research efforts will lead to a novel low-cost, high fidelity wireless distributed sensing system for multiple walker recognition and tracking. As an alternative to video camera systems, iSMART systems can be widely deployed to automatically monitor airports, customs / harbors, and other critical national infrastructures. This project will also generate interesting hands-on labs on intelligent sensor / sensor networks and class projects for both undergraduate and graduate students.

虽然高成本、数据密集型的多摄像机系统已被广泛用于移动的人体跟踪和识别，但热释电红外（PIR）传感器具有各种优点，包括显著低成本、化学稳定性、对人体热变化的高灵敏度和极低的传感器数据吞吐量。该项目实现了智能压缩多步行者识别和跟踪（iSMART）基于PIR传感器网络（PSN）的测试床。iSMART的创新之处包括三个方面：（1）上下文感知的感兴趣区域（RoI）探索，以实现传感器覆盖面积和信息采集分辨率之间的内在权衡。本研究使用严格的数学模型来测量ROI上下文。(2)用于网络智能的分散式推理/学习。本计画发展一个以信念传播为基础的分布式推论机制，并将其与资料至物件关联，以进行多个步行者的连续追踪与辨识。它使用基于正交投影的分布式学习进行传感器校准和特征模型训练。(3)网络化的压缩采样结构和传感协议。本研究将压缩和多路传感理论的最新进展应用于新型网络传感器接收器模式和分布式传感协议的设计，并将为多个步行者的识别和跟踪提供一种低成本、高保真度的无线分布式传感系统。作为摄像机系统的替代方案，iSMART系统可以广泛部署，以自动监控机场，海关/港口和其他重要的国家基础设施。该项目还将为本科生和研究生提供关于智能传感器/传感器网络的有趣的动手实验室和课堂项目。