| In the near future, we envision many mobile robots working for or interacting with humans in various scenarios such as guided shopping, policing, and senior care. In these scenarios, tracking humans and mobile robots is a crucial enabling technology as it provides their continuous locations and identities. We study each kind of tracking individually as humans and mobile robots differ in appearance, mobility, capability of computation and sensing, and feasibility of cooperation with tracking systems. In addition to these distinctions, the scale of a tracking system's environment is another key factor that determines the appropriate tracking algorithm. In small scale environments such as halls and rooms, tracking systems need to be lightweight with real-time performance. In large scale environments such as campuses and entire buildings, tracking systems face complex and noisy backgrounds. Tracking systems need to be robust and scalable. This dissertation examines problems for human and mobile robot tracking in both small and large scale environments in order to enable future human-robot applications.;First, we study human tracking. In small scale environments like halls, multiple cameras are typically deployed to monitor most areas. We need to track multiple humans across multiple cameras accurately in real-time. We present VM-Tracking, a tracking system that achieves these goals. VM-Tracking aggregates motion sensor information from humans' mobile devices, which accompany them everywhere, and integrates it with visual data from physical locations. In large scale human tracking, determining humans' identities among different visual surveillance scenes is a major challenge, especially with noisy and incorrect data. Since people's mobile devices connect to widespread cellular networks, we propose EV-Matching to address these practical challenges. EV-Matching is a large scale human tracking system that matches humans recorded in both cellular network and visual surveillance datasets. It achieves efficient and robust tracking.;Next, we study mobile robot tracking. Similar to human tracking, visual cameras are widely available in small scale environments. Mobile robots are capable of computation and cooperation but their on-board sensing capacities have room for improvement. Thus, we present S-Mirror, a novel approach using infrastructural cameras to "reflect" sensing signals towards mobile robots, which greatly extends their sensing abilities. S-Mirror is a lightweight infrastructure that is easily deployed as it leverages existing visual camera networks, leaving most computation to robots. In large scale mobile robot tracking, scalability is a major challenge as infrastructure does not cover all areas and augmenting it is expensive. Since robots have built-in sensing capabilities, we propose BridgeLoc, a novel vision-based robot tracking system that integrates both robots' and infrastructural camera views. We achieve accurate view bridging via visual tags, images with special patterns. We develop a key technology, rotation symmetric visual tag design, that greatly extends BridgeLoc's scalability.;In this dissertation, we study human and mobile robot tracking in small and large scale environments. We design and implement all of the above systems. Our real-world experimental evaluations show the advantages of our work and demonstrate its potential for human-robot applications. |
