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\subsection{Computer Vision in Traffic Applications}  Computer vision is used extensively in traffic applications as an instrument of data collection and monitoring. The two primary branches of computer vision in traffic applications include presence detection (sometimes referred to as virtual loops) andfeature  tracking. Presence detection has widespread commercial application due to its has a relatively  high degree of reliability; reliability, on par with more common sensors such as inductive loops;  its primary application is in providing traffic counts, queue lengths, and basic presence detection. Feature tracking Tracking  is a more complex application which tracks moving features aims to extract the road users' trajectories  within the  camera space continuously, providing accurate measures field  of position, velocity, view, from which velocity  and acceleration over time, but may be derived: it  is therefore  generally less reliable than presence detection systems. The NGSIM project was one of the first large-scale video data collection projects making use of semi-automated vehicle tracking from freeway and urban arterials video data to obtain vehicle trajectories for traffic model calibrations \cite{Kim_2005}. Surrogate safety analysis also makes use ofextensive  trajectory data, for example with the early SAVEME project \cite{Ervin_2000,Gordon_2012}, and now more recently with extensive research open source  projects such as Traffic-Intelligence Traffic Intelligence  \cite{saunier06feature-based,Jackson_2013}. \subsection{Tracking Optimisation}  \cite{Ettehadieh_2014}