deletions | additions       diff --git a/section_Results_subsection_Traffic_Data__.tex b/section_Results_subsection_Traffic_Data__.tex  index d1b00e6..589d6da 100644  --- a/section_Results_subsection_Traffic_Data__.tex  +++ b/section_Results_subsection_Traffic_Data__.tex 
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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' 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 sites (1 and 2), 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). In the same table, the parameters obtained from the genetic algorithm were evaluated on different scenarios of the same video sequence:  - A ‘control set’ from the other five minutes of annotated video 
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(insert diagram showing example of close/far here or in methodology)  This was done in an attempt to verify whether or not overfitting over-fitting  was occurring. It was found that, while the improvement was sometimes greater for the sequence on which the optimization was executed, there was a significant positive upward trend in performance on the control set. A point of mention is that the MOTA of the close and far zones do not always average to the same result as the full analysis zone. This is explained by the small errors created at the borders of the analysis zone as vehicles enter and leave. \subsection{Optimized Parameters Applied on Seperate Datasets} 
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