deletions | additions       diff --git a/section_Reviewer_Comments_for_Authors__.tex b/section_Reviewer_Comments_for_Authors__.tex  index ab4afb6..917e2d3 100644  --- a/section_Reviewer_Comments_for_Authors__.tex  +++ b/section_Reviewer_Comments_for_Authors__.tex 
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1. I basically think that the technical contribution of the window-based filtering is weak. However, this approach may be adopted in other re-identification methods to improve the performance. My main concern at this point is how agnostic this performance improvement is to different combinations of pedestrian detection and re-identification. If the approach much depends on the combination, providing some insights on when this approach works well is essential. Some additional experimental results to compare different combinations together with discussion on them is necessary.  \end{quote}  \textit{We understand the reviewers concern. We think that having pedestrian detections of another PD algorithm for all the 75207 frames of the dataset would be interesting way to confirm how agnostic to the algorithms employed the performance boost from the window-based classifier is.}  \textit{We \textit{However we  feel there are some parts of the performance boost that we can readily state will always happen, such as:} \begin{enumerate}  \item \textit{When the parameter d is >1 (minimum number of re-identifications greater than 1 for a window to be provided as output) then spurious False Positives and spurious mis-classifications are always filtered out, thus improving precision.}  \item \textit{When the parameter w is >2 (minimum width of the window greater than 2) then missed detections or mis-classifications that fall between d correct re-identifications will always be recovered, since that whole window is provided as output.}