  Context awareness is an essential part of mobile and ubiquitous computing.
Its goal is to unveil situational information about mobile users like locations
and activities. The sensed context can enable many services like navigation,
AR, and smarting shopping. Such context can be sensed in different ways
including visual sensors. There is an emergence of vision sources deployed
worldwide. The cameras could be installed on roadside, in-house, and on mobile
platforms. This trend provides huge amount of vision data that could be used
for context sensing. However, the vision data collection and analytics are
still highly manual today. It is hard to deploy cameras at large scale for data
collection. Organizing and labeling context from the data are also labor
intensive. In recent years, advanced vision algorithms and deep neural networks
are used to help analyze vision data. But this approach is limited by data
quality, labeling effort, and dependency on hardware resources. In summary,
there are three major challenges for today's vision-based context sensing
systems: data collection and labeling at large scale, process large data
volumes efficiently with limited hardware resources, and extract accurate
context out of vision data. The thesis explores the design space that consists
of three dimensions: sensing task, sensor types, and task locations. Our prior
work explores several points in this design space. We make contributions by (1)
developing efficient and scalable solutions for different points in the design
space of vision-based sensing tasks; (2) achieving state-of-the-art accuracy in
those applications; (3) and developing guidelines for designing such sensing
systems.
