deletions | additions       diff --git a/section_Experimental_Results_subsection_The__.tex b/section_Experimental_Results_subsection_The__.tex  index 8a9d647..0a5c599 100644  --- a/section_Experimental_Results_subsection_The__.tex  +++ b/section_Experimental_Results_subsection_The__.tex 
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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 and 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).  \begin{table}  \caption{}  \label{tab:performance1}  \begin{tabular}{lcccccccccc}  &S1S & &S1W & &S2 & &S3V1 & &S3V2 &\\  &MOTA &MOTP &MOTA &MOTP &MOTA &MOTP &MOTA &MOTP &MOTA &MOTP\\  \hline  Default parameters & & & & & & & & & &\\  \hline  First 5 min &0.74595 &1.642 &0.04107 &3.001 &0.70318 &1.1 &0.75976 &1.114 &0.75042 &1.34\\  Last 5 min &0.67878 &1.49 &0.04501 &2.742 &0.56925 &1.119 &0.76262 &0.971 &0.85619 &1.175\\  Full 10 min &0.71905 &1.581 &0.02898 &4.594 &0.70024 &1.092 &0.69966 &1.23 &0.63395 &1.519\\  Close &0.70611 &1.378 &0.04254 &2.666 &0.64724 &1.251 &0.75359 &1.149 &0.82001 &1.101\\  Far &0.63198 &1.819 &0.03138 &3.524 &0.71861 &1.009 &0.76088 &1.074 &0.66971 &1.612\\  \hline  \hline  % &S1S & &S1W & &S2 & &S3V1 & &S3V2 &\\  % &MOTA &MOTP &MOTA &MOTP &MOTA &MOTP &MOTA &MOTP &MOTA &MOTP\\  Optimized parameters on first 5~min & & & & & & & & & &\\  \hline  First 5 min &0.90855 &1.233 &0.70759 &1.974 &0.81178 &1.019 &0.85527 &1.000 &0.85092 &0.736\\  Last 5 min &0.88377 &1.233 &0.69288 &1.885 &0.71742 &0.853 &0.77248 &0.842 &0.69091 &0.585\\  Full 10 min &0.9045 &1.237 &0.70993 &1.920 &0.76673 &0.918 &0.81746 &0.927 &0.78852 &0.666\\  Close &0.87103 &1.174 &0.67357 &1.979 &0.61695 &0.856 &0.78374 &0.857 &0.87462 &0.713\\  Far &0.82119 &1.298 &0.60789 &1.971 &0.76047 &0.978 &0.73834 &1.040 &0.67978 &0.554\\  \end{tabular}  \end{table}  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  - The full 10 minutes of annotated video 
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The issue with genetic algorithms is there is no guarantee of obtaining the optimal solution. There is a possibility that the results converged towards a local maximum. It also does not factor in the relationships that may exist between separate parameters. However, short of a random search which would require unreasonable or infinite amounts of computation time, there is no optimization function that guarantees convergence. Therefore the genetic algorithm was deemed to be adequate for the sake of this research.  Another source of error is within the correlation of the parameters with MOTA. Considering the method used to generate the trajectories for each sequence, the genetic algorithm may have remained on a similar set of parameters for many generations which may bias the correlation. The higher resolution cameras also had very few data points for lower MOTA values as seen in the first section of the results. While this has an impact of the numerical value of the correlation, it supports the conclusion that higher resolution cameras rely less on optimal parameters to provide reliable tracking results.