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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 benefit comes from allowing the use of a 'pre-trained-off-the-shelf' pedestrian detector. The user may not have access to re-training the pedestrian detector, but will for sure be able to include given samples (false positive samples) under a new class in the gallery of the re-identification algorithm.}  \textit{To drive this point home with the readers we've enriched the first paragraph of section 3.2 False Positives Class thusly:}  \textbf{\textit{Pedestrian detector algorithms usually produce some false positive detections. One way to improve their performance in a given context is to retrain the detector adding the false positives to the training as hard negatives on that specific context. However, some false positives may still remain, or the user may wish to use a 'pre-trained-off-the-shelf' PD algorithm where retraining is not an option.}}}  \textbf{\textit{Therefore,}}  \textbf{\textit{another contribution of our work is to adapt the RE-ID module to deal with the FP produced by the PD.}}  \textit{and added the following sentence in that same section:}  \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.}}