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\begin{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?  \end{quote}  \textit{That's exactly it! The False Positive class is indeed an extra set of training samples to allow the classifier to have a trained class of false positives. The novelty comes from the fact that in the Re-Identification field no-one else has done so before.} before. We've added the following to "False Positives Class" subsection to make it clearer.}  \textbf{\textit{Observing that the appearance of the FPs in a given scenario is not completely random, but is worth modeling (see Fig 5),}}  \textbf{\textit{we provide the re-identification classifier with \ac{FP} samples for it to be able to train a \ac{FP} class.}}  \