Pedestrian detection and gender recognition utilizing YOLO and CNN algorithms

Research Article
Open access

Pedestrian detection and gender recognition utilizing YOLO and CNN algorithms

Zixuan Zhong 1*
  • 1 Software and Systems engineering, Lappeenranta University of Technology, Kela, 53851, Finland    
  • *corresponding author Zixuan.Zhong@student.lut.fi
Published on 31 January 2024 | https://doi.org/10.54254/2755-2721/31/20230136
ACE Vol.31
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-287-9
ISBN (Online): 978-1-83558-288-6

Abstract

As crowd-based activities continue to surge in locales such as markets and restaurants, the significance of understanding pedestrian flow is increasingly evident. Over recent years, advancements in dynamic pedestrian detection, facilitated by the YOLO (You Only Look Once) algorithm, have seen widespread application in areas like crowd management and occupancy estimation. The YOLO algorithm has demonstrated high accuracy and efficiency in real-time object tracking and counting. However, for specific use cases, data derived solely from monitoring pedestrian flows may prove inadequate. This study presents YOLO-Gender, a system leveraging YOLO and Convolutional Neural Network (CNN) for pedestrian tracking and gender classification. The objective is to enhance the richness of data extracted from surveillance camera footage, thus rendering it more valuable for societal applications. The YOLO suite of algorithms, hailed for their superior performance and rapid iteration speed, is among the most extensively utilized tools in the field. The proposed system is predicated on YOLO v8, the most advanced iteration of the YOLO algorithm, released in 2023, which boasts its highest accuracy to date.

Keywords:

YOLO v8, pedestrian tracking, gender classification, Convolutional Neural Network, transfer learning

Zhong,Z. (2024). Pedestrian detection and gender recognition utilizing YOLO and CNN algorithms. Applied and Computational Engineering,31,133-138.
Export citation

References

[1]. Vallimeena P, Gopalakrishnan U, Nair B B, et al. 2019 Detection of Human Face Attributes using CNN Algorithms – A Survey Proc. 2019 International Conference on Intelligent Computing and Control Systems (ICCS) (IEEE) pp. 576-581

[2]. Hou C. 2023 Human Detection Based on YOLOv5 Application Highlights in Science, Engineering and Technology vol. 34 pp. 203-208Karahan M, Lacinkaya F, Erdonmez K, et al. 2022 Age and Gender Classification from Facial Features and Object Detection with Machine Learning Journal of Fuzzy Extension and Applications vol. 3(3) pp. 219-230

[3]. Sumit S S, Awang Rambli D R, Mirjalili S, et al. 2022 Improving the Performance of Tiny-YOLO-Based CNN Architecture for Human Detection Applications Applied Sciences vol. 12(18) pp. 9331

[4]. Priya K P L, Jyothirmai I, Akshaya G, et al. 2023 Identification of Autism in Children Using Static Facial Features and Deep Neural Networks Turkish Journal of Computer and Mathematics Education (TURCOMAT) vol. 14(2) pp. 704-715

[5]. Jabraelzadeh P, Charmin A, Ebadpore M. 2020 Hybrid Method for Face Detection, Gender Recognition, Facial Landmarks Localization and Pose Estimation Using Deep Learning to Improve Accuracy Journal of Artificial Intelligence in Electrical Engineering vol. 8(32) pp. 1-14

[6]. Vishwakarma H, Verma G, Singh S, et al. 2019 Single Shot Multi-Face Detection & Gender Recognition Proc. 2nd International Conference on Advanced Computing and Software Engineering (ICACSE)

[7]. Gawande U, Hajari K, Golhar Y. 2022 SIRA: Scale Illumination Rotation Affine Invariant Mask R-CNN for Pedestrian Detection Applied Intelligence vol. 52(9) pp. 10398-10416

[8]. Lee S, Hwang J, Kim J, et al. 2023 CNN-Based Crosswalk Pedestrian Situation Recognition System Using Mask-R-CNN and CDA Applied Sciences vol. 13(7) pp. 4291

[9]. Nagajyothi D, Charan P S, Zeeshan M, et al. 2023 Image Enhancement for Pedestrian Detection at Night Time Proc. 2023 2nd International Conference for Innovation in Technology (INOCON) (IEEE) pp. 1-7

Cite this article

Zhong,Z. (2024). Pedestrian detection and gender recognition utilizing YOLO and CNN algorithms. Applied and Computational Engineering,31,133-138.

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：978-1-83558-287-9(Print) / 978-1-83558-288-6(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.31
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]. Vallimeena P, Gopalakrishnan U, Nair B B, et al. 2019 Detection of Human Face Attributes using CNN Algorithms – A Survey Proc. 2019 International Conference on Intelligent Computing and Control Systems (ICCS) (IEEE) pp. 576-581

[2]. Hou C. 2023 Human Detection Based on YOLOv5 Application Highlights in Science, Engineering and Technology vol. 34 pp. 203-208Karahan M, Lacinkaya F, Erdonmez K, et al. 2022 Age and Gender Classification from Facial Features and Object Detection with Machine Learning Journal of Fuzzy Extension and Applications vol. 3(3) pp. 219-230

[3]. Sumit S S, Awang Rambli D R, Mirjalili S, et al. 2022 Improving the Performance of Tiny-YOLO-Based CNN Architecture for Human Detection Applications Applied Sciences vol. 12(18) pp. 9331

[4]. Priya K P L, Jyothirmai I, Akshaya G, et al. 2023 Identification of Autism in Children Using Static Facial Features and Deep Neural Networks Turkish Journal of Computer and Mathematics Education (TURCOMAT) vol. 14(2) pp. 704-715

[5]. Jabraelzadeh P, Charmin A, Ebadpore M. 2020 Hybrid Method for Face Detection, Gender Recognition, Facial Landmarks Localization and Pose Estimation Using Deep Learning to Improve Accuracy Journal of Artificial Intelligence in Electrical Engineering vol. 8(32) pp. 1-14

[6]. Vishwakarma H, Verma G, Singh S, et al. 2019 Single Shot Multi-Face Detection & Gender Recognition Proc. 2nd International Conference on Advanced Computing and Software Engineering (ICACSE)

[7]. Gawande U, Hajari K, Golhar Y. 2022 SIRA: Scale Illumination Rotation Affine Invariant Mask R-CNN for Pedestrian Detection Applied Intelligence vol. 52(9) pp. 10398-10416

[8]. Lee S, Hwang J, Kim J, et al. 2023 CNN-Based Crosswalk Pedestrian Situation Recognition System Using Mask-R-CNN and CDA Applied Sciences vol. 13(7) pp. 4291

[9]. Nagajyothi D, Charan P S, Zeeshan M, et al. 2023 Image Enhancement for Pedestrian Detection at Night Time Proc. 2023 2nd International Conference for Innovation in Technology (INOCON) (IEEE) pp. 1-7

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.