Passive Perception and Path Tracking of Tourists in Mountain Scenic Spots Through Face to Body Two Stepwise Method

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

this paper proposes a face to body two-stepwise iterative method for the passive perception and tracking of tourists. The path of tourists can be obtained. It is an interesting work.  However, the following issues should be revised:(1) in The experiments,  The number of tourists is too small. We would like to see the scene with more tourists in order to verify the algorithm in this paper. (2) In the whole process, whether the cooperation of multiple cameras is considered to ensure the continuity of tracking. I hope to elaborate on this in the article.

Author Response

Dear reviewers，

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

In this work, a face to body (F2B) two-stepwise iterative method is proposed for for the passive perception and tracking of tourists. The proposed method is carried as follows: the method starts with faces and bodies of tourists which are extracted from real-time video streams using the RetinaFace and YOLOX models, respectively. Then, the facial features of the target tourists are extracted using the ArcFace model. These features and linked with the faces detected by RetinaFace. Thereafter, the Hungarian algorithm is utilized for constructing multi-features database, which is used to match the faces and bodies of the same tourist. Finally, the Fast-ReID model and a spatial position algorithm are utilized for the re-identification of tourist targets and the tracking their dynamic paths.

 

The manuscript is interesting; however, the following comments need to be addressed:

1 – In the abstract, the problem statement needs to be stated clearly.

2 – There are some typos need to be checked. For example “art (SOTA)”, and “… targets, and and various, such as …” .

3 – End the keywords with period “.” .

4 – For the list of contribution :

a – Add space before the first word.

b – Capitalize the first letter of the first word.

c – The contribution requires more elaboration .

5 – In Table 1, replace “No” by “ID” for better interpretation .

6 – In Table 1,

a- the link of the dataset 4 is not working. Please check and included accessible link .

b- the link of the dataset 5 is not working. Please check and included accessible link .

c- the link of the dataset 6 is not working. Please check and included accessible link .

7 – Equations from other source should have credit .

8 – Pseudo code of the proposed method need to be included which will make the proposed method more clear to the readers .

9 – In Figure 6, move the detected face into a place where it can be more clear to the readers .

10 – Check comment 9 for Figures 7 and 8.

11 – The improvement in accuracy is jumped from 31.6% to 89%. It requires more elaboration and supplementary file for the results can be included for more clarity .

12 – There are some object detection algorithms need to be included to improve the manuscript:

a – Xie, Xingxing, et al. "Oriented R-CNN for object detection." Proceedings of the IEEE/CVF international conference on computer vision. 2021.

b – Faisal, Mustafa M., et al. "Object detection and distance measurement using AI." 2021 14th International Conference on Developments in eSystems Engineering (DeSE). IEEE, 2021.

c – Diwan, Tausif, G. Anirudh, and Jitendra V. Tembhurne. "Object detection using YOLO: Challenges, architectural successors, datasets and applications." multimedia Tools and Applications 82.6 (2023): 9243-9275.

13 – The manuscript has several typos and grammatical errors. The authors need to check and correct those errors .

14 – There are references ends with et al., please check the journal guideline for authors regarding the references .

 

 

 

 

 

Comments on the Quality of English Language

The manuscript has several typos and grammatical errors. The authors need to check and correct those errors .

Author Response

Dear reviewers，

   Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Please find the attached file for the comments. 

Comments for author File: Comments.pdf

Comments on the Quality of English Language

Minor editing of English language required.

Author Response

Dear reviewers,

  Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

In this work, a face to body (F2B) two-stepwise iterative method is proposed for for the passive perception and tracking of tourists. The proposed method is carried as follows: the method starts with faces and bodies of tourists which are extracted from real-time video streams using the RetinaFace and YOLOX models, respectively. Then, the facial features of the target tourists are extracted using the ArcFace model. These features and linked with the faces detected by RetinaFace. Thereafter, the Hungarian algorithm is utilized for constructing multi-features database, which is used to match the faces and bodies of the same tourist. Finally, the Fast-ReID model and a spatial position algorithm are utilized for the re-identification of tourist targets and the tracking their dynamic paths.

 

In the revised manuscript, the authors have addressed the raised comments.

Reviewer 3 Report

Comments and Suggestions for Authors

The authors addressed all my comments. I don't have any further comments on the paper. 

Comments on the Quality of English Language

Minor editing of English language required.