deletions | additions       diff --git a/section_Methodology_The_approach_proposed__.tex b/section_Methodology_The_approach_proposed__.tex  index 8b2e834..4e0f218 100644  --- a/section_Methodology_The_approach_proposed__.tex  +++ b/section_Methodology_The_approach_proposed__.tex 
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TODO define MOTA (with matching distance)  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 $\theta$  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 optimized by the genetic algorithm.  \begin{table} 
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The relationship between the parameters and MOTA is evaluated using Spearman's Rank Correlation Coefficient, calculated as:  \begin{center}$\rho$ \begin{displaymath}\rho  = 1-$\frac{6\sum d_{i}^{2}}{n(n^{2}-1)}$\right  \end{center} 1-\frac{6\sum d_{i}^{2}}{n(n^{2}-1)}  \end{displaymath}  \subsection{Effect of MOTA on Traffic Data} 
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