Skip to main content
SpringerLink
Log in

Density Estimation of Heterogeneous Crowd in Mass Religious Gatherings Using Image Processing and Denoising Filter

  • Conference paper
  • First Online:
Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1393))

  • 458 Accesses

Abstract

Crowd density is an essential parameter of crowd dynamics that can be used to manage the crowd, predict pedestrian movement patterns, and minimize crowd risk situations. Inadequate crowd control measures in a mass gathering can eventually lead to crowd disaster. A potential solution to avoid the risk is to predict high-density crowds and prevent them before they turn fatal. Bombay Dharamshala in Ujjain is one such crowded place, where pilgrims usually choose to halt during Kumbh Mela—the world’s largest mass religious gathering. For this study, CCTV footage of Bombay Dharamshala collected on 8-May-2016 during Kumbh Mela is used. The motive of this study is to assess the performance of a deep-learning-based Image Processing Module (IPM) combined with a denoising filter in estimating pedestrian density in the video footage of a highly heterogeneous crowd. The pedestrian count as predicted by the IPM is fed into the denoising filter for noise reduction. The model used for detecting pedestrians is unique, accounting for the crowd’s heterogeneity to benefit Indian mass gathering scenarios. The combined Pedestrian Count Model (PCM) shows an accuracy of 87.28%.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Government decreases death toll in cambodian stampede (2010). http://edition.cnn.com/2010/WORLD/asiapcf/11/25/cambodia.festival.deaths/index.html

  2. Gambrell J (2015) Saudi crush was deadliest hajj tragedy ever

    Google Scholar 

  3. Panic erupts during champions league viewing in Italy, injuring 1500 (2017). https://bnonews.com/index.php/2017/06/panic-erupts-during-champions-league-viewing-in-italy-injuring-1500/

  4. Illiyas F, Mani S, Pradeepkumar A, Mohan K (2013) Human stampedes during religious festivals: a comparative review of mass gathering emergencies in India. Int J Disaster Risk Reduct 5:10–18. https://doi.org/10.1016/j.ijdrr.2013.09.003

    Article  Google Scholar 

  5. Gayathri H, Aparna P, Verma A (2017) A review of studies on understanding crowd dynamics in the context of crowd safety in mass religious gatherings. Int J Disaster Risk Reduct. https://doi.org/10.1016/j.ijdrr.2017.07.017

    Article  Google Scholar 

  6. Helbing D (1997) Verkehrsdynamik. Springer

    Google Scholar 

  7. Kormanova A (2013) A review on macroscopic pedestrian flow modelling. Acta Inform Prag 2(2):39–50

    Article  Google Scholar 

  8. Burstedde C, Klauck K, Schadschneider A, Zittartz J (2001) Simulation of pedestrian dynamics using a two-dimensional cellular automaton. Phys A Stat Mech Appl 295:507525. https://doi.org/10.1016/S0378-4371(01)00141-8

    Article  MATH  Google Scholar 

  9. Helbing D, Farkas I, Vicsek T (2000) Simulating dynamic features of escape panic. Nature 407:487490. https://doi.org/10.1038/35035023

    Article  Google Scholar 

  10. Helbing D, Johansson A, Al-Abideen HZ (2007) Dynamics of crowd disasters: an empirical study. Phys Rev E, statistical, nonlinear, and soft matter physics 75:046109. https://doi.org/10.1103/PhysRevE.75.046109

  11. Oberhagemann D, Konnecke R, Schneider V (2014) Effect of social groups on crowd dynamics: empirical findings and numerical simulations. Springer, Cham, pp 12511258. https://doi.org/10.1007/978-3-319-02447-9_103

  12. Hou YL, Pang G (2010) Human detection in crowded scenes. In: 2010 IEEE international conference on image processing, pp 721–724. https://doi.org/10.1109/ICIP.2010.5651982

  13. Brostow GJ, Cipolla R (2006) Unsupervised bayesian detection of independent motion in crowds. In: Proceedings of the 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR 06), vol 1. New York, USA, pp 594–601. https://doi.org/10.1109/CVPR.2006.320

  14. Loy CC, Gong S, Xiang T (2013) From semi-supervised to transfer counting of crowds. In: Proceedings of the 2013 IEEE International Conference on Computer Vision, ICCV 13. Sydney, Australia , pp 2256–2263. https://doi.org/10.1109/ICCV.2013.270

  15. Idrees H, Saleemi I, Seibert C, Shah M (2013) Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the 2013 IEEE conference on computer vision and pattern recognition (CVPR13). Portland, USA, pp 2547–2554. https://doi.org/10.1109/CVPR.2013.329

  16. Lowe DG (1999) Object recognition from local scale-invariant features. In: Proceedings of the international conference on computer vision, ICCV 99, vol 2. Washington, USA. http://dl.acm.org/citation.cfm?id=850924.851523

  17. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Proceedings of the 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR05), vol 1. San Diego, USA, pp 886–893. https://doi.org/10.1109/CVPR.2005.177

  18. Ojala T, Pietikainen M, Maenpaa T (2000) Gray scale and rotation invariant texture classification with local binary patterns. In: Lecture notes in computer science, vol 1842, pp 404–420. https://doi.org/10.1007/3-540-45054-8_27

  19. Bourdev L, Maji S, Malik J (2011) Describing people: a poselet-based approach to attribute classification. In: Proceedings of the 2011 international conference on computer vision, ICCV 11. Barcelona, Spain, pp 1543–1550. https://doi.org/10.1109/ICCV.2011.6126413

  20. Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R, Lecun Y (2013) Overfeat: integrated recognition, localization and detection using convolutional networks. In: International conference on learning representations (ICLR) (Banff). https://arxiv.org/abs/1312.6229

  21. Yao H, Han K, Wan W, Hou L (2017) Deep spatial regression model for image crowd counting. CoRR abs/1710.09757. http://arxiv.org/abs/1710.09757

  22. Shang C, Ai H, Bai B (2016) End-to-end crowd counting via joint learning local and global count. In: 2016 IEEE international conference on image processing proceedings. Phoenix, USA, pp 1215–1219. https://doi.org/10.1109/ICIP.2016.7532551

  23. Zhang Y, Zhou D, Chen S, Gao S, Ma YS (2016) Single-image crowd counting via multi-column convolutional neural network. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR). Las Vegas, USA, pp 589–597. https://doi.org/10.1109/CVPR.2016.70

  24. Zhang C, Li H, Wang X, Yang X (2015) Cross-scene crowd counting via deep convolutional neural networks. In: The IEEE conference on computer vision and pattern recognition (CVPR). Boston, USA. https://doi.org/10.1109/CVPR.2015.7298684

  25. Boominathan L, Kruthiventi SSS, Babu RV (2016) Crowdnet: a deep convolutional network for dense crowd counting. In: Proceedings of the 24th ACM international conference on multimedia, MM 16. New York, USA, pp 640–644. https://doi.org/10.1145/2964284.2967300

  26. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. CoRR. https://arxiv.org/abs/1409.1556

  27. Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Patt Anal Mach Intell 39. https://doi.org/10.1109/TPAMI.2016.257703

  28. Kalman RE (1960) A new approach to linear filtering and prediction problems. J Basic Eng 82:35–45. https://doi.org/10.1115/1.3662552

    Article  MathSciNet  Google Scholar 

  29. Welch G, Bishop G (1995) An introduction to the kalman filter

    Google Scholar 

  30. Julier S, Uhlmann J (1999) A new extension of the kalman filter to nonlinear systems. Proc. SPIE 3068. https://doi.org/10.1117/12.280797

  31. Kumar SV (2017) Traffic flow prediction using kalman filtering technique. In: Procedia engineering, vol 187. Vilnius, Lithuania, pp 582–587. https://doi.org/10.1016/j.proeng.2017.04.417

Download references

Acknowledgements

The work reported in this paper is part of the project titled “The Kumbh Mela Experiment: Measuring and Understanding the Dynamics of Mankind’s largest crowd,” funded by the Ministry of Electronics and IT, Government of India (MITO-0105); Netherlands Organization for Scientific Research, NWO (Project no. 629.002.202); and Robert Bosch Center for Cyber-Physical Systems, Indian Institute of Science, Bangalore (Grant No. RBCO001). The authors also express their gratitude toward the Kumbh Mela administration and government of Madhya Pradesh, India, for providing constant support and official permissions to carry out research work and establish an Indo-Dutch collaboration research camp at Kumbh Mela 2016.

Author information

Authors and Affiliations

  1. Department of Cyber Physical Systems, Indian Institute of Science, Bangalore, India

    Nipun Choubey

  2. Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, State College, Pennsylvania, USA

    Abhishek Kumar Prajapati

  3. Department of Civil Engineering, Indian Institute of Science, Bangalore, India

    Ashish Verma

  4. Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, India

    Anirban Chakraborty

Authors
  1. Nipun Choubey
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abhishek Kumar Prajapati
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ashish Verma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anirban Chakraborty
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ashish Verma .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India

    Aruna Tiwari

  2. Computer Science and Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India

    Kapil Ahuja

  3. Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, India

    Anupam Yadav

  4. Department of Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

  5. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, India

    Kusum Deep

  6. School of Mathematics, Computer Science and Engineering, Liverpool Hope University, Liverpool, UK

    Atulya K. Nagar

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Choubey, N., Prajapati, A.K., Verma, A., Chakraborty, A. (2021). Density Estimation of Heterogeneous Crowd in Mass Religious Gatherings Using Image Processing and Denoising Filter. In: Tiwari, A., Ahuja, K., Yadav, A., Bansal, J.C., Deep, K., Nagar, A.K. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1393. Springer, Singapore. https://doi.org/10.1007/978-981-16-2712-5_36

Download citation

Publish with us

Policies and ethics

Search

Navigation