deletions | additions       diff --git a/section_Methodology_subsection_Site_Selection__.tex b/section_Methodology_subsection_Site_Selection__.tex  index 735ccdb..0826e4c 100644  --- a/section_Methodology_subsection_Site_Selection__.tex  +++ b/section_Methodology_subsection_Site_Selection__.tex 
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The video data is then processed with state-of-the-art computer vision software developed specifically for surrogate safety analysis applications. The feature-based tracker from the open-source Traffic Intelligence project\footnote{\url{https://bitbucket.org/Nicolas/trafficintelligence/}} extracts all road user trajectories from image space \cite{Saunier_2006}, providing Cartesian coordinates of all moving objects within the field of view at the camera frame rate, typically thirty times a second. This high-resolution data of all road users within a traffic scene is needed for the road user behaviour analysis to follow. Given the large volume of data to be processed, additional software\footnote{\url{https://bitbucket.org/pstaub/tvalib/}} is used to partially calibrate and automate the analysis and to annotate the traffic scene with contextual metadata \cite{St_Aubin_2015}.  Example trajectory data extracted from two cameras at one Swedish  site is shown in Figure~\ref{fig:Trajectory_data}. In this case two cameras are placed in the north-west corner to provide full and overlapping coverage of the roundabout.