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\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. \end{quote}  \textit{We thank the reviewer for pointing this out. If this matter is not clear, we shall endeavor to \textbf{make} it clear.}  \textit{In figure 2, we assume the Single-frame Classifier is a classifier that will classify input images as one of the person classes available in the training set. Therefor, when a classifier that only has classes for people, receives an input image of a fire-extinguisher (figure 5) it always fails to correctly classify (there is no correct class!).}  \textit{In Section 5.2.2 Single frame Re-Identification we cite [19] as the algorithm employed to enact classification. [19] describes a multi-class classifier, that does not, by default, contains an extra class for unclassifiable inputs. Therefore, when using the "FP Class module", we are in fact providing the re-identification classifier algorithm with extra training samples for it to train an extra class, a class of detection false positives.}  \textit{For this to be clearer we have edited the 5.4 Scenarios section thusly:}  \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{}  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? 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?