deletions | additions       diff --git a/section_Reviewer_Comments_for_Authors__.tex b/section_Reviewer_Comments_for_Authors__.tex  index 4bcfc15..e057e0b 100644  --- a/section_Reviewer_Comments_for_Authors__.tex  +++ b/section_Reviewer_Comments_for_Authors__.tex 
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\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}  \  \begin{quote}  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.  
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\textit{Afterwards, in the FPCLASS scenario, we turn ON theFPclass, thus providing the re-identification algorithm withadditional training samples (samples of false positives) to buildan extra class (a false positive class), and therefor evaluate ourapproach to address detection false positives.}\textbf{}  \begin{quote}  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?  \end{quote}  3. Object detectors are often trained with hard negatives, which is false negatives detected with an initial (or tentative) detector as in Dalal’s HOG paper. I think positive false class in this paper is somehow related hard negatives. I think some discussion on this point is necessary. For example, the detector can be trained with an augmented dataset that contains images in Figure 5. Given this, what are the advantages of the proposed false positive class over retraining the detector?