deletions | additions       diff --git a/section_Methodology_The_approach_proposed__.tex b/section_Methodology_The_approach_proposed__.tex  index 5d20e91..4f1f595 100644  --- a/section_Methodology_The_approach_proposed__.tex  +++ b/section_Methodology_The_approach_proposed__.tex 
...
A genetic algorithm with the chosen parameters is then run on 5 minutes of each video sequence with a corresponding ground truth. Computer vision is used to create files containing the trajectory information which is then run through the Pvatools filtering functions. The optimization process searches for the tracking parameters θ that maximize the performance of the of the tracked trajectories compared to the ground truth inventory. The evaluation metric used to rank performance is the Multiple Object Tracking Accuracy (MOTA) as described in \cite{Bernardin_2008}. It is the most common metric for tracking accuracy used in computer vision. Once the genetic algorithm finds a local maximum, the resulting set of parameters is applied to the other video sequences to determine the performance of the tracking parameters under different conditions.  Table~\ref{tab:parameters} is an overview of the parameters used in optimized by  the genetic algorithm. \begin{table}  \caption{Parameters Used in Optimization Process}  \label{tab:parameters} 
...
\subsection{Performance Results}  The MOTA performance results are dependent on the maximum distance allowed between a tracked trajectory and the ground truth. In this study, a 5m 5~m  distance is used as it is approximately the length of a large family passenger  vehicle. Multiple Object Tracking Precision (MOTP) is another performance metric used to find the average distance between the ground truth and tracked object road users  \cite{Bernardin_2008}. This is particularly important when measuring traffic data such as time-to-collision (TTC), gap time and vehicle interactions as they require a certain level of precision to acquire reliable results. The relationship between the parameters and MOTA is evaluated using Spearman's Rank Correlation Coefficient, calculated as: 
...