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\section{Experimental Results}  \subsection{The Relationship of Tracking Accuracy with Traffic Data}  In the first phase of optimization, the genetic algorithm was run for the full 10~min for each video sequence. Each generation had The population size is set to  20 specimens, individuals, with  a score threshold of 0.2 for natural deaths, a maximum of 60 percent 60~\%  survivors, a crossover rate of 60 percent 60~\%  and a mutation rate of 5 percent. 5~\%.  Each specimen contained a file that was saved and evaluated in terms set  of tracking parameters (individual) and the corresponding trajectories generated by the tracker are saved: the  traffic count counts  and average speed speeds  at the entrance and exit of each lane of the analysis zone, represented in figure (insert figure showing counts/speed). zone are extracted and analyzed with respect to tracking accuracy (see FIGURE~).  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.  
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