Tracking multiple mobile devices in CCTV-enabled areas

Thesis

Over the last decade, we have witnessed an unprecedented interest in indoor positioning technologies, with a variety of solutions developed in academic and industrial research labs. Although the field has reached a significant level of maturity, there is still no dominant solution and, as a consequence, positioning services are still lacking in many buildings. In order for a solution to be widely implemented and adopted, two key requirements must be satisfied: low cost and high accuracy. The dichotomy between cost and accuracy has fragmented the technology landscape, leading to a plethora of competing solutions that cannot satisfy both requirements simultaneously.

The key objective of this thesis is to investigate how to unify the two disparate camps, providing high positioning accuracy with very low cost. Many approaches have tried to achieve this goal by fusing different sensor modalities. However, the majority of existing work has only investigated how to fuse sequences of measurements for which the associations with the targets are known (i.e. device personal data). Sensor fusion techniques that combine device personal data and anonymous sensor streams (where the associations between the measurements and the targets are not known) remain under-explored as of today. In this thesis, we investigate how to efficiently combine device sensor data and anonymous sensor streams from various sensor modalities in order to build low cost and high accuracy positioning systems. By combining these two types of sensor modalities in one system we see a great potential in designing cost-effective and accurate positioning systems for challenging environments such as for tracking people in highly dynamic industrial settings. Our goal is to design a multi-target multi-sensor tracking framework which will utilise existing sensor infrastructure found in industrial environments and large public buildings (e.g museums) in order to provide reliable positioning services.

Authors: Papaioannou, S

• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford