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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. Additionally, 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}.  The objective of this paper is to improve the performance of feature-based tracking in merging zones of roundabouts through parameterization using a genetic algorithm comparing tracked trajectories to sets of manually annotated ground truths. There is an emphasis on cross-validating sets of parameters on separate datasets with similar properties, as well as exploring the performance on datasets with different properties. This paper will provide a brief overview of the current state of computer vision and calibration in traffic applications, then detail the methodology including the ground truth inventory, measures of performance and calibration procedure, followed by a presentation and discussion of the results and summarized in the conclusion with recommendations for future research.