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\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. The population size is set to 20 individuals, with a score threshold of 0.2 for natural deaths, a maximum of 60~\% survivors, a crossover rate of 60~\% and a mutation rate of 5~\%. All the sets of tracking parameters (individuals) and the corresponding trajectories generated by the tracker are saved over the whole optimization process: the traffic counts and average speeds at the entrance and exit of each lane of the analysis zone are extracted and analyzed with respect to tracking accuracy (see FIGURE~).   \begin{figure}  \begin{tabular}{rcc}  & Counts & Average Speeds\\  S1S & \includgraphics[width=0.45\textwidth]{figures/s1s-flow.pdf} & \includgraphics[width=0.45\textwidth]{figures/s1s-speed.pdf} \\  S1W & \includgraphics[width=0.45\textwidth]{figures/s1w-flow.pdf} & \includgraphics[width=0.45\textwidth]{figures/s1w-speed.pdf} \\  S2 & \includgraphics[width=0.45\textwidth]{figures/s2-flow.pdf} & \includgraphics[width=0.45\textwidth]{figures/s2-speed.pdf} \\  S3V1 & \includgraphics[width=0.45\textwidth]{figures/s3v1-flow.pdf} & \includgraphics[width=0.45\textwidth]{figures/s3v1-speed.pdf} \\  S3V2 & \includgraphics[width=0.45\textwidth]{figures/s3v2-flow.pdf} & \includgraphics[width=0.45\textwidth]{figures/s3v2-speed.pdf} \\  \end{tabular}  \label{fig:mota-traffic-data}  \caption{Counts (left column) and average speeds (right column) measured at the entrance and exits of the merging zone for each video sequence (corresponding to each row)}  \end{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.   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. 
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