deletions | additions       diff --git a/section_Results_subsection_Traffic_Data__.tex b/section_Results_subsection_Traffic_Data__.tex  index c60f6b1..32de881 100644  --- a/section_Results_subsection_Traffic_Data__.tex  +++ b/section_Results_subsection_Traffic_Data__.tex 
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\section{Results}  \subsection{Traffic Data Compared \section{Experimental Results}  \subsection{The Relationship of Tracking Accuracy  with MOTA} Traffic Data}  In the first phase of optimization, the genetic algorithm was run for the full 10 minutes 10~min  for each video sequence with a corresponding ground truth. sequence.  Each generation had 20 specimens, a score threshold of 0.2 for natural deaths, a maximum of 60 percent survivors, a crossover rate of 60 percent and a mutation rate of 5 percent. Each specimen contained a file that was saved and evaluated in terms of traffic count and average speed at the entrance and exit of each lane of the analysis zone, represented in figure (insert figure showing counts/speed). These results were used to validate the choice of MOTA as a measure of performance. As expected, there is a strong correlation between MOTA and the number of objects tracked due to the nature of MOTA. It is important to note that the total vehicle counts are not necessarily comparable to the number of objects in the ground truth since the genetic algorithm is run on the analysis zones which do not include the whole video. Lower MOTA results also tend to have a larger range in terms of average tracked speed. As the performance increases in lanes that have a significant number of vehicles, the average tracked speeds converge. Therefore, the video chosen for annotation should have a reasonable flow in every lane of the analysis zone.   The two higher resolution cameras (S3V1 and S3V2) had very few results of poor performance. This suggests a lower sensitivity to the chosen parameters used in the genetic algorithm. S1W presents a special case where only 49 percent of specimens had any tracked objects. These two cases are discussed in subsequent sections.  \subsection{Parameter Correlation} \subsection{Correlation of Tracking Parameters with Tracking Accuracy}  Each unique MOTA from the various optimizations had associated parameters from which Spearman's rank correlation coefficient could be extracted. The parameters were individually compared to the MOTA and presented in Table~\ref{tab:spearman}. The winter camera for Site 1 had many results in which no objects were tracked and thus had a MOTA of 0. These sets of parameters were removed from the analysis to obtain a more representative correlation. The number of samples also varied based off of computation time since the higher resolution cameras took over 12 hours for the 20 samples of each generation of the genetic algorithm.  \begin{table} 
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