Structured Tracking for Safety, Security, and Privacy: Algorithms for Fusing Noisy Estimates from Sensor, Robot, and Camera Networks

摘要

Emerging developments in the speed, size, and power requirements of processors, coupled with networking advances, enable new applications for networks of sensors, cameras, and robots. However, we live in a world filled with uncertainty and noise, which affects the sensors we use, the environments we model, and the objects we observe. In this dissertation, we define Structured Tracking, where we apply novel machine learning and inference techniques to leverage environmental and tracked object structure. This approach improves accuracy and robustness while reducing computation. We focus on three application areas of societal benefit: safety, security, and privacy. We apply Belief Propagation (BP) (148) algorithms to sensor networks, and describe our modular framework for the more general Reweighted-BP formulation (203).