Advanced people flow monitoring and gender classification: A joint application of YOLO, DeepSORT, and convolutional neural networks

Research Article
Open access

Advanced people flow monitoring and gender classification: A joint application of YOLO, DeepSORT, and convolutional neural networks

Qianjun Li 1*
  • 1 University of California San Diego, San Diego, California, CA 92093, USA    
  • *corresponding author qil008@ucsd.edu
Published on 31 January 2024 | https://doi.org/10.54254/2755-2721/30/20230113
ACE Vol.30
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-285-5
ISBN (Online): 978-1-83558-286-2

Abstract

In an era of increasingly crowd-focused activities, understanding the dynamics of people's flow is of paramount importance. However, data derived solely from detecting and counting individuals can prove inadequate for certain use-cases. Addressing this deficiency, this study introduces a robust method to enrich data extraction, thereby enhancing its value to a wide range of stakeholders. Presented herein is a novel system dubbed YOLO-Gender, an innovative integration of YOLO and CNN, designed to deliver comprehensive people tracking and gender classification. This gender recognition component provides a much-needed edge in crowd management and facilitates efficient planning of gender-specific services. The core foundation of the system is built upon YOLOv8, the apex of the YOLO model series, renowned for its unparalleled accuracy and efficiency. Through the use of transfer learning models pre-trained on ImageNet, gender recognition is achieved, showcasing a marked enhancement over conventional CNN models. Assessments of this system validate its robust performance, underlining its potential for large-scale deployment. This study represents a significant step forward in AI-powered surveillance, offering a solution that effectively enriches and analytically processes extracted data.

Keywords:

YOLO, CNN, YOLO-Gender

Li,Q. (2024). Advanced people flow monitoring and gender classification: A joint application of YOLO, DeepSORT, and convolutional neural networks. Applied and Computational Engineering,30,279-283.
Export citation

References

[1]. Amin, M. S., Wang, C., & Jabeen, S. (2022). Fashion sub-categories and attributes prediction model using deep learning. The Visual Computer, 1-14.

[2]. Huu, P. N., Tien, D. N., & Thanh, K. N. (2022). Action recognition application using artificial intelligence for smart social surveillance system. J. Inf. Hiding Multim. Signal Process., 13(1), 1-11.

[3]. Gündüz, M. Ş., & Işık, G. (2023). A new YOLO-based method for social distancing from real-time videos. Neural Computing and Applications, 1-11.

[4]. Sugianto, N., Tjondronegoro, D., Stockdale, R., & Yuwono, E. I. (2021). Privacy-preserving AI-enabled video surveillance for social distancing: Responsible design and deployment for public spaces. Information Technology & People.

[5]. Rezaei, M., & Azarmi, M. (2020). Deepsocial: Social distancing monitoring and infection risk assessment in covid-19 pandemic. Applied Sciences, 10(21), 7514.

[6]. Tsai, J. K., Hsu, C. C., Wang, W. Y., & Huang, S. K. (2020). Deep learning-based real-time multiple-person action recognition system. Sensors, 20(17), 4758.

[7]. Genaev, M. A., Komyshev, E. G., Shishkina, O. D., Adonyeva, N. V., Karpova, E. K., Gruntenko, N. E., ... & Afonnikov, D. A. (2022). Classification of fruit flies by gender in images using smartphones and the YOLOv4-tiny neural network. Mathematics, 10(3), 295.

[8]. Zhu, X., Xu, H., Zhao, Z., Wang, X., Wei, X., Zhang, Y., & Zuo, J. (2021). An environmental intrusion detection technology based on WiFi. Wireless Personal Communications, 119(2), 1425-1436.

[9]. Cojocea, E., Hornea, S., & Rebedea, T. (2019, October). Balancing between centralized vs. edge processing in IoT platforms with applicability in advanced people flow analysis. In 2019 18th RoEduNet Conference: Networking in Education and Research (RoEduNet) (pp. 1-6). IEEE.

[10]. Ward, T. (2023). Development of Detection and Tracking Systems for Autonomous Vehicles Using Machine Learning (Doctoral dissertation, Morehead State University).

Cite this article

Li,Q. (2024). Advanced people flow monitoring and gender classification: A joint application of YOLO, DeepSORT, and convolutional neural networks. Applied and Computational Engineering,30,279-283.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-285-5(Print) / 978-1-83558-286-2(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.30
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[1]. Amin, M. S., Wang, C., & Jabeen, S. (2022). Fashion sub-categories and attributes prediction model using deep learning. The Visual Computer, 1-14.

[2]. Huu, P. N., Tien, D. N., & Thanh, K. N. (2022). Action recognition application using artificial intelligence for smart social surveillance system. J. Inf. Hiding Multim. Signal Process., 13(1), 1-11.

[3]. Gündüz, M. Ş., & Işık, G. (2023). A new YOLO-based method for social distancing from real-time videos. Neural Computing and Applications, 1-11.

[4]. Sugianto, N., Tjondronegoro, D., Stockdale, R., & Yuwono, E. I. (2021). Privacy-preserving AI-enabled video surveillance for social distancing: Responsible design and deployment for public spaces. Information Technology & People.

[5]. Rezaei, M., & Azarmi, M. (2020). Deepsocial: Social distancing monitoring and infection risk assessment in covid-19 pandemic. Applied Sciences, 10(21), 7514.

[6]. Tsai, J. K., Hsu, C. C., Wang, W. Y., & Huang, S. K. (2020). Deep learning-based real-time multiple-person action recognition system. Sensors, 20(17), 4758.

[7]. Genaev, M. A., Komyshev, E. G., Shishkina, O. D., Adonyeva, N. V., Karpova, E. K., Gruntenko, N. E., ... & Afonnikov, D. A. (2022). Classification of fruit flies by gender in images using smartphones and the YOLOv4-tiny neural network. Mathematics, 10(3), 295.

[8]. Zhu, X., Xu, H., Zhao, Z., Wang, X., Wei, X., Zhang, Y., & Zuo, J. (2021). An environmental intrusion detection technology based on WiFi. Wireless Personal Communications, 119(2), 1425-1436.

[9]. Cojocea, E., Hornea, S., & Rebedea, T. (2019, October). Balancing between centralized vs. edge processing in IoT platforms with applicability in advanced people flow analysis. In 2019 18th RoEduNet Conference: Networking in Education and Research (RoEduNet) (pp. 1-6). IEEE.

[10]. Ward, T. (2023). Development of Detection and Tracking Systems for Autonomous Vehicles Using Machine Learning (Doctoral dissertation, Morehead State University).

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.