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

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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.