deletions | additions       diff --git a/section_Introduction_The_use_of__.tex b/section_Introduction_The_use_of__.tex  index 91ab445..6f18baf 100644  --- a/section_Introduction_The_use_of__.tex  +++ b/section_Introduction_The_use_of__.tex 
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Despite the undeniable progress of the video sensors and computer vision algorithms in their varied transportation applications, there persists a distinct lack of large comparisons of the performance of video sensors in varied conditions defined for example by the complexity of the traffic scene (movements and mix of road users), the characteristics of cameras~\cite{Wan_2014} and their installation (height, angle), the environmental conditions (e.g.\ the weather)~\cite{Fu_2015}, etc. This is particularly hampered by the poor characterization of the datasets used for performance evaluation and the limited availability of benchmarks and public video datasets for transportation applications~\cite{saunier14dataset}. Tracking performance is often reported using ad hoc and incomplete metrics such as ``detection rates'' instead of standard and more suitable metrics such as CLEAR MOT~\cite{Bernardin_2008}. Finally, the computer vision algorithms are typically manually adjusted by trial and error using a small dataset covering few conditions affecting performance while the reported performance evaluated on the same dataset is thus over-estimated: comparing to other fields such as machine learning, it should be clear that the algorithms should be systematically optimized on a calibration dataset, while performance should be reported for a separate validation dataset~\cite{ettehadieh15systematic}.   While the performance of video sensors for more simple traffic variables has been more extensively studied, not all factors have been systematically analyzed and the issues with parameter optimization and the lack of separate calibration and validation datasets abound. are widespread.  Besides, the relationship of tracking performance with performance for traffic parameters has never been investigated. The objective of this paper is first to improve the performance of existing detection and tracking methods for video data in terms of accuracy of tracking, but also different kinds of traffic data such as counts, speeds, gaps and road user interactions. This is done through the optimization of tracking parameters using a genetic algorithm comparing the tracker output with manually annotated trajectories. The method is applied to a set of traffic videos extracted from a large surrogate safety study of roundabout merging zones~\cite{st-aubin15big-data}, covering factors such as the distance of road users to the camera, two types of cameras, the camera resolution and two weather conditions. The second objective is to explore the transferability of parameters for separate datasets with the same properties (consecutive video samples) and across different properties, by reporting how optimizing tracking for one condition impacts performance in terms of tracking and traffic parameters for the other conditions. This paper is a follow up on \cite{ettehadieh15systematic} that investigates more factors and how tracking performance is related to the accuracy of traffic parameters.  
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