deletions | additions       diff --git a/section_Results_subsection_Traffic_Data__.tex b/section_Results_subsection_Traffic_Data__.tex  index 589d6da..d335c48 100644  --- a/section_Results_subsection_Traffic_Data__.tex  +++ b/section_Results_subsection_Traffic_Data__.tex 
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\subsection{Over-fitting Analysis}  The second phase of the validation process was to evaluate the performance of optimized parameters compared to the default parameters. The genetic algorithm was run for the first five minutes of each video file for between 10 and 30 generations over the course of one to three days depending on the site. Table (insert table about optimized parameters) is a summary of the optimal parameters found for these specific sequences. Some parameters such as 'min-feature-distanceklt', 'mm-connection-distance' and 'mm-tracking-error' 'min-tracking-error'  seem to have converged towards similar values on three of the cameras. However, it is apparent that in most cases the optimal solution is unique. The results of the genetic algorithm are presented in table (insert table with mota & motp) as a comparison between the performance of the default and optimized parameters. Both the MOTA and MOTP values for every case are significantly improved. The results from the ‘winter camera’ in particular express the need for optimization as the MOTA did not reach over 0.05 using default parameters whereas a potential for a MOTA over 0.70 was found. As expected, the two sites with a lower correlation with the parameters did not improve as much as the other three sites. A lower MOTA did not necessarily mean that the camera cannot be properly optimized. When comparing S1S & S2, which have similar physical properties and identical cameras, there was a noteworthy difference (0.905 and 0.797 respectively). The source of the difference could be due to the lower number of vehicles in the site 2 video segment or a difference in traffic compositions (i.e. presence of a higher volume of trucks). 
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