Nicolas Saunier edited section_Methodology_The_approach_proposed__.tex  almost 9 years ago

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\end{tabular}  \end{table}  The tracking parameters listed in TABLE~\label{tab:parameters} are optimized using a genetic algorithm that aims to improve tracking accuracy, comparing the tracker output to the ground truth for a video sequence (see overview in FIGURE~\ref{fig:optimization_overview}. Each iteration of the genetic algorithm corresponds a set (population) of individuals with each individual representing a complete set of tracking parameters $\boldface{\theta}$: $\boldsymbol{\theta}$:  the tracker and filtering routines are run on the video sequence for each set of tracking parameters $\boldface{\theta}$. $\boldsymbol{\theta}$.  The tracker output and the ground annotations are compared in the analysis zone and the genetic algorithm will generate a new population of tracking parameters by favoring and combining the best tracking parameters of the previous population. The metric of tracking performance is the Multiple Object Tracking Accuracy (MOTA) as described in \cite{Bernardin_2008}. It is the most common metric for tracking accuracy, i.e.\ to evaluate the whole trajectory and not just detections in each frame, used in computer vision. MOTA is basically the ratio of the number of correct detections of each object over the number of frames in which the object appears (in the ground truth): 

\end{itemize}  Transferability is thus verified by applying each set of optimized tracking parameters to each of the other annotated sequences. The full ten minute annotated videos are used to calculate the MOTA, which are then compared to the optimized tracking performance and reported also as a percentage of the maximum MOTA. This is done to avoid potential bias from the site selection and vehicle composition, which may lower the MOTA result compared to other cameras, despite having similar relative tracking performance results.