  Multiple people tracking is a key problem for many applications such as
surveillance, animation or car navigation, and a key input for tasks such as
activity recognition. In crowded environments occlusions and false detections
are common, and although there have been substantial advances in recent years,
tracking is still a challenging task. Tracking is typically divided into two
steps: detection, i.e., locating the pedestrians in the image, and data
association, i.e., linking detections across frames to form complete
trajectories.
  For the data association task, approaches typically aim at developing new,
more complex formulations, which in turn put the focus on the optimization
techniques required to solve them. However, they still utilize very basic
information such as distance between detections. In this thesis, I focus on the
data association task and argue that there is contextual information that has
not been fully exploited yet in the tracking community, mainly social context
and spatial context coming from different views.
