deletions | additions       diff --git a/Methodology Video Data.tex b/Methodology Video Data.tex  index 5aabaef..1ada6b3 100644  --- a/Methodology Video Data.tex  +++ b/Methodology Video Data.tex 
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\begin{itemize}  \item \textbf{Parallax error} is mitigated by maximising the subtending angle between the camera and the height of tracked objects. In practical terms this requires a high angle of view or ideally a bird's eye view, tracking objects with a small height to base ratio. Passenger cars are generally more forgiving in this respect than heavy vehicles or pedestrians.   \item \textbf{Pixel resolution} determines measurement precision. Objects further away from the camera experience lower tracking precision than objects near the camera. Error due to pixel resolution is mitigated by placing the camera as close to the study area as possible and using high-resolution cameras, though increases in resolution offer diminishing returns of tracking accuracy.   \item Finally, \textbf{tracking errors} may occur with scene visibility issues or due to limits with current computer vision techniques. These erroneous observations have to be rejected or reviewed manually. Some attempts Attempts  have been made in recent literature and for this work  at validating and optimising tracking accuracy using search algorithms and the  Measures Of Tracking Accuracy (MOTA) methodology \cite{ettehadieh15systematic}. MOTA is a measure of accuracy that combines multiple sources of error simultaneously at a great level of detail, including false positive and false negative detection  as measures well as spatio-temporal accuracy  of performance \cite{ettehadieh15systematic}. This method measurements along entire trajectories in camera space. The tracking optimisation  is replicated for this study using a genetic algorithm that searches heuristically for optimal tracking parameters using MOTA analysis as a measure of fitness from manually annotated video data using Urban Tracker \cite{Jodoin_2014}. See section \ref{tracking_calibration} for details and  results. \end{itemize}  Depending on the steps taken to minimize tracking errors, feature-based tracking functions best over study areas of up to 50-100~m in length with high-to-medium speed, low-to-medium density flows.