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\section{Reviewer Comments for Authors:}  \subsection{Reviewer: 1}  \begin{quote}  \textbf{What are the contributions of the paper:} This paper proposes a filtering approach to improve re-identification, which may be used upon pedestrian detection. I think that the paper does not provide sufficient detail to reproduce the results, and its technical contributions is weak; however, the presented results might be of interest for some readers. Therefore, I made my recommendation re-submit after some modifications.  \end{quote}  \begin{quote}  \textbf{What are the additional ways in which the paper could be improved:}   \end{quote}  \begin{quote}  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 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 for a window to be considered greater than 1) 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.}  \end{enumerate}  \textbf{What are the additional ways in which the paper could be improved:} 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.  2. The details of the main technical components of this work are not provided. As for false positive class, I don’t see how it is used in the re-id system. As for window-based classifier, the paper does not mention what its outputs are (and I assume that they are binary classification results, not ranking). The paper definitely needs these details. Particularly what if there are multiple people in a single frame?