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The use of video data for automatic analysis has been on an upward trend as more powerful computational tools and detection and tracking technology become available. The improvement of computer vision is particularly important in surrogate safety analysis such as in \cite{St_Aubin_2013} where automated tracking of trajectories is used to evaluate the safety performance of merge zones on highway ramps. \cite{Moreno_2013} performed a study evaluating speed as a measure of safety performance where specialized equipment needed to be installed. The Ministère des Transports du Québec already close to full video coverage of all the highways in the city of Montreal. With proper computer vision technology, speed profiles could be developed and evaluated at larger scale than was previously feasible.  While flow rates and speed counts can be assessed through various means, computer vision is one of the most efficient methods of measuring other roadway interactions. Many studies are seeking to analyze microscopic movements such as \cite{St_Aubin_2013} in roundabouts and \cite{Hill_2015} in relation to driver behavior. At this scale, accurately representing the exact characteristics of the moving vehicle is crucial to provide accurate analyses of surrogate safety indicators such as gap time, time-to-collision(TTC) and user pairs.  The performance of computer vision tools are not consistent depending on several factors such as the resolution and type of camera \cite{Wan_2014} and the weather conditions \cite{Fu_2015}.  fixed/mobile cameras