A Human Face Detector for Big Data Analysis of Pilgrim Flow Rates in Hajj and Umrah

Authors

  • Riad Alharbey Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Saudi Arabia
  • Ameen Banjar Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Saudi Arabia
  • Yahia Said Department of Electrical Engineering, College of Engineering, Northern Border University, Saudi Arabia https://orcid.org/0000-0003-0613-4037
  • Mohamed Atri College of Computer Sciences, King Khalid University, Saudi Arabia
  • Mohamed Abid CES Laboratory, ENIS, University of Sfax, Tunisia
Volume: 14 | Issue: 1 | Pages: 12861-12868 | February 2024 | https://doi.org/10.48084/etasr.6668

Abstract

In today's digital world, some crowded venues still rely on outdated methods, such as counting people using counters or sensors at the entrance. These techniques generally fail in areas where people move randomly. Crowd management is an important challenge for ensuring human safety. This paper focuses on developing a crowd management system for Hajj and Umrah duty. Motivated by the recent artificial intelligence techniques and the availability of large-scale data, a crowd management system was established and is presented in this paper. Utilizing the most recent Deep Learning techniques, the proposed crowd management system will be charged with detecting human faces, face identification, tracking, and human face counting tasks. Face counting and detection will be achieved by computing the number of people in a given area. Face detection and tracking will be carried out for person identification, flow rate estimation, and security. The suggested crowd management system is composed of three key components: (1) face detection, (2) assignment of a specific identifier (ID) to each detected face, (3) each detected face will be compared to the stored faces in the dataset. If the detected face is identified, it will be assigned to its ID, or a new ID will be assigned. The crowd management system has been developed to improve the Cross-Stage Partial Network (CSPNet) with attention module integration. An attention module was employed to address object location challenges and a channel-wise attention module for determining the objects of focus. Extensive experiments on the WIDER FACE dataset proved the robustness of the proposed face detection module, which allows for building reliable crowd management and flow rate estimation systems through detecting, tracking, and counting human faces. The reported results demonstrated the power of the proposed method while achieving high detection performance in terms of processing speed and detection accuracy.

Keywords:

face counting, face tracking, deep learning, attention module, channel-wise module, crowd management, Hajj, Umrah

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References

M. Afif, R. Ayachi, E. Pissaloux, Y. Said, and M. Atri, "Indoor objects detection and recognition for an ICT mobility assistance of visually impaired people," Multimedia Tools and Applications, vol. 79, no. 41, pp. 31645–31662, Nov. 2020.

J. Niu, Q. Hu, Y. Niu, T. Zhang, and S. Kumar Jha, "Real-Time Recognition and Location of Indoor Objects," Computers, Materials & Continua, vol. 68, no. 2, pp. 2221–2229, 2021.

A. C. Hernandez, C. Gomez, J. Crespo, and R. Barber, "Object Detection Applied to Indoor Environments for Mobile Robot Navigation," Sensors, vol. 16, no. 8, Aug. 2016, Art. no. 1180.

L. Jiang, W. Nie, J. Zhu, X. Gao, and B. Lei, "Lightweight object detection network model suitable for indoor mobile robots," Journal of Mechanical Science and Technology, vol. 36, no. 2, pp. 907–920, Feb. 2022.

C. Prandi, B. R. Barricelli, S. Mirri, and D. Fogli, "Accessible wayfinding and navigation: a systematic mapping study," Universal Access in the Information Society, vol. 22, no. 1, pp. 185–212, Mar. 2023.

M. Salemdeeb and S. Erturk, "Multi-national and Multi-language License Plate Detection using Convolutional Neural Networks," Engineering, Technology & Applied Science Research, vol. 10, no. 4, pp. 5979–5985, Aug. 2020.

J. Cao, J. Zhang, and X. Jin, "A Traffic-Sign Detection Algorithm Based on Improved Sparse R-cnn," IEEE Access, vol. 9, pp. 122774–122788, 2021.

A. Alsheikhy, Y. Said, and M. Barr, "Logo Recognition with the Use of Deep Convolutional Neural Networks," Engineering, Technology & Applied Science Research, vol. 10, no. 5, pp. 6191–6194, Oct. 2020.

Y. Said, M. Barr, and H. E. Ahmed, "Design of a Face Recognition System based on Convolutional Neural Network (CNN)," Engineering, Technology & Applied Science Research, vol. 10, no. 3, pp. 5608–5612, Jun. 2020.

R. Ayachi, M. Afif, Y. Said, and A. B. Abdelaali, "Pedestrian detection for advanced driving assisting system: a transfer learning approach," in 5th International Conference on Advanced Technologies for Signal and Image Processing, Sousse, Tunisia, Sep. 2020, pp. 1–5.

C.-Y. Wang, H.-Y. Mark Liao, Y.-H. Wu, P.-Y. Chen, J.-W. Hsieh, and I.-H. Yeh, "CSPNet: A New Backbone that can Enhance Learning Capability of CNN," in IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA, Jun. 2020, pp. 1571–1580.

S. Yang, P. Luo, C. C. Loy, and X. Tang, "WIDER FACE: A Face Detection Benchmark," in IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, Jun. 2016, pp. 5525–5533.

I. T. Nafea, "Simulation of crowd management using deep learning algorithm," International Journal of Web Information Systems, vol. 17, no. 4, pp. 321–332, Jan. 2021.

A. Khan, J. Ali Shah, K. Kadir, W. Albattah, and F. Khan, "Crowd Monitoring and Localization Using Deep Convolutional Neural Network: A Review," Applied Sciences, vol. 10, no. 14, Jan. 2020, Art. no. 4781.

U. Bhangale, S. Patil, V. Vishwanath, P. Thakker, A. Bansode, and D. Navandhar, "Near Real-time Crowd Counting using Deep Learning Approach," Procedia Computer Science, vol. 171, pp. 770–779, Jan. 2020.

W. Albattah, M. H. Kakakhel, S. Habib, M. Islam, S. Khan, and K. Kadir, "Hajj Crowd Management Using CNN-Based Approach," Computers, Materials & Continua, vol. 66, no. 2, pp. 2183–2197, Jan. 2021.

N. Wijermans, C. Conrado, M. van Steen, C. Martella, and J. Li, "A landscape of crowd-management support: An integrative approach," Safety Science, vol. 86, pp. 142–164, Jul. 2016.

S. Lamba and N. Nain, "Crowd Monitoring and Classification: A Survey," in Advances in Computer and Computational Sciences, S. K. Bhatia, K. K. Mishra, S. Tiwari, and V. K. Singh, Eds. New York, NY, USA: Springer, 2017, pp. 21–31.

R. Jiang et al., "DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events," in 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA, Aug. 2019, pp. 2114–2122.

E. B. Varghese and S. M. Thampi, "Application of Cognitive Computing for Smart Crowd Management," IT Professional, vol. 22, no. 4, pp. 43–50, Jul. 2020.

E. B. Varghese, S. M. Thampi, and S. Berretti, "A Psychologically Inspired Fuzzy Cognitive Deep Learning Framework to Predict Crowd Behavior," IEEE Transactions on Affective Computing, vol. 13, no. 2, pp. 1005–1022, Apr. 2022.

C. Wang, H. Zhang, L. Yang, S. Liu, and X. Cao, "Deep People Counting in Extremely Dense Crowds," in 23rd ACM international conference on Multimedia, Brisbane, Australia, Oct. 2015, pp. 1299–1302.

M. Poblet, E. Garcia-Cuesta, and P. Casanovas, "Crowdsourcing Tools for Disaster Management: A Review of Platforms and Methods," in International Workshop on AI Approaches to the Complexity of Legal Systems, Bologna, Italy, Dec. 2013, pp. 261–274.

J. L. Abbott and M. W. Geddie, "Event and Venue Management: Minimizing Liability Through Effective Crowd Management Techniques," Event Management, vol. 6, no. 4, pp. 259–270, Apr. 2000.

K. Khan, W. Albattah, R. U. Khan, A. M. Qamar, and D. Nayab, "Advances and Trends in Real Time Visual Crowd Analysis," Sensors, vol. 20, no. 18, Jan. 2020, Art. no. 5073.

G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, "Densely Connected Convolutional Networks," in IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, Jul. 2017, pp. 2261–2269.

R. Ayachi, M. Afif, Y. Said, and M. Atri, "Strided Convolution Instead of Max Pooling for Memory Efficiency of Convolutional Neural Networks," in 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications, Maghreb, Tunisia, Dec. 2018, pp. 234–243.

X. Zeng, X. Peng, Y. Wang, and Y. Qiao, "Finding hard faces with better proposals and classifier," Machine Vision and Applications, vol. 31, no. 7, Sep. 2020, Art. no. 61.

Z. Zhang, W. Shen, S. Qiao, Y. Wang, B. Wang, and A. Yuille, "Robust Face Detection via Learning Small Faces on Hard Images," in IEEE Winter Conference on Applications of Computer Vision, Snowmass, CO, USA, Mar. 2020, pp. 1350–1359.

T. M. Hoang, G. P. Nam, J. Cho, and I.-J. Kim, "DEFace: Deep Efficient Face Network for Small Scale Variations," IEEE Access, vol. 8, pp. 142423–142433, 2020.

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How to Cite

[1]
R. Alharbey, A. Banjar, Y. Said, M. Atri, and M. Abid, “A Human Face Detector for Big Data Analysis of Pilgrim Flow Rates in Hajj and Umrah”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 1, pp. 12861–12868, Feb. 2024.

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