46 lines
3.6 KiB
Plaintext
46 lines
3.6 KiB
Plaintext
|
|
Dear Reviewer,
|
|
|
|
-> At first we would like to start with a short overview over all changes. The individual answers follow directly after this text. All additions to the text are highlighted in blue. Words or text passages suggested by the reviewers to be removed are highlighted in red. As you will see, abstract and introduction are completely revised to better highlight the novel contributions. We were able to implement many suggestions of the reviewers. The transition was highly extended, to achieve a better understanding of the method. We have also added a detailed description of how and by what means our system is installed in a building. This also leads to a better description of the experimental setup.
|
|
We added a complete new section, evaluating the activity recognition. Additionally, you will find mind smaller changes and addition throughout the paper as well as further improvements of the writing. In the following our answers are marked with "->".
|
|
|
|
|
|
Overall:
|
|
|
|
From my point of view, it is not clear that this paper represents a novel contribution. Almost all the bases and formulation have been presented in your previous paper (IPIN 2016, FUSION 2016, ISPRS International Journal of Geo-Information 2017). Only the KDL optimization and a the trials in a new environment are novels, and they obtained better results due to the floor adaptation of the WAF model.
|
|
-> To clarify the contributions of this work, we have added a listing in lines xxx to xxx. We hope that this will help to provide a better overview.
|
|
|
|
Detailed comments:
|
|
|
|
line 187: The smoothing Monte Carlo filter is the same of you proposed in IPIN 2016 conference? It is not clear why you are referring it as Condensation. Are you using any concrete Condensation implementation (OpenCV, matlab,...), and this is the explanation?.
|
|
|
|
Condensation filter is used in the field of visual tracking due to the researchers do not access to the agent information. In your case you have access to the phone sensors, therefore the concept is a Monte Carlo Localization with transitions detection based on steps and orientation detection.
|
|
|
|
Using a MCL you do not need to mix observations and actions in the same concept (eq 3.), you should divide into observations and transitions.
|
|
|
|
From my point of view formulation of transition model T should be tackled using actions (steps) and observations (s_wifi) should be used like an observation model V (described like "Evaluation" in section 5).
|
|
|
|
line 226: What is z_t? Is an observation o_t? nomenclature should be unified through the whole paper
|
|
-> we completely changed the formulation here. z_t is the z-component at time t, which belongs to the state q_t.
|
|
it is given be the nav-mesh triangles, which denote the buildings floor.
|
|
z_t stays constant, as long as the floor is flat. it only changes when stairs are involved.
|
|
|
|
line 319: Are these thresholds able for all of pedestrians? have you tried with different actors and behaviors?
|
|
|
|
line 410: Why 10.000 samples in the building? Should it be dependent of the building size, wifi noise, etc...?
|
|
|
|
line 480: In line 410 you propose 10.000 particles and in the experiments propose 1.000, why?
|
|
|
|
line 508: results shown in Figure 3 are not clear presented, from my point of view the proposal seems to be worse than the previous one, there are much more outliers (blue color)
|
|
|
|
line 512: typo "prober"
|
|
|
|
Figure 5: It is not clear connections between ground floor and first floor, is there any typo or figures are misplaced?
|
|
|
|
Figure 6: You use the expression "Monte Carlo", are you referring to Condensation?
|
|
|
|
Results section: results and comments are ad-hoc for this environment, and it is not demonstrated that could be applied in a more general context.
|
|
|
|
|
|
|