Skip to main content
SpringerLink
Log in

A texture based mani-fold approach for crowd density estimation using Gaussian Markov Random Field

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The objective of this work is to leverage the clues obtained from mani-fold sources, to figure out the density of people existent in exceptionally dense crowded regions. The complications in crowd density estimation include perspective effect, occlusion, clutter background, textured brick surface, and few pixels per target. In crowd scenarios with these complications, detection based techniques are not accurate, even none of the single feature alone is suitable to estimate crowd density. Therefore, our methodology depends on mani-fold sources such as head detection with low confidence, recurrence of texture elements by using frequency domain, wavelet and scale invariant feature transform (SIFT) descriptor to measure the density count. The information obtained from manifold sources is used to train a support vector machine (SVM), which generates a patch count estimation. Next, a Gaussian-based Markov Random Field (MRF) is applied on image patches to obtain uniformity on crowd count. The Gaussian MRF furnishes the discrepancy in crowd count along with local neighborhoods at multiple scales. We tested our approach on four different datasets such as Shanghai Tech_A, UCF_CC_50, extended UCF_CC_100 and UCSD. The former three datasets are a crisp contrast to existing crowd datasets used in literature which contains almost hundreds or tens of individuals in crowd images. The latter UCSD dataset is used to test the robustness of our technique in low-density crowd too. We compare the proposed method with both traditional and convolutional neural network (CNN) based approaches. Low computational complexity indicates that the proposed technique provides decent performance rate and can be employed in real-world applications. Our experimental results validate the adequacy and efficiency of the intended methodology by measuring the density of crowd images.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Arandjelovic O (2008) Crowd detection from still images. In: BMVC 2008: proceedings of the british machine vision association conference 2008. BMVA press, pp 1–10

  2. Ariffin A, D’Oorazio C, Choo KKR, Slay J (2013) ios forensics: how can we recover deleted image files with timestamp in a forensically sound manner?. In: 2013 eighth international conference on availability, reliability and security (ARES). IEEE, pp 375–382

  3. Ariffin A, Slay J, Choo KK (2013) Data recovery from proprietary formatted CCTV hard disks. In: IFIP international conference on digital forensics. Springer, pp 213–223

  4. Azencott R, Wang J-P, Younes L (1997) Texture classification using windowed fourier filters. IEEE Trans Pattern Anal Mach Intell 19(2):148–153

    Article  Google Scholar 

  5. Bansal A, Venkatesh K (2015) People counting in high density crowds from still images. arXiv:1507.08445

  6. Brostow GJ, Cipolla R (2006) Unsupervised bayesian detection of independent motion in crowds. In: 2006 IEEE computer society conference on computer vision and pattern recognition, vol 1. IEEE, pp 594–601

  7. Chan AB, Vasconcelos N (2009) Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th international conference on computer vision. IEEE, pp 545–551

  8. Chan AB, Liang Z-SJ, Vasconcelos N (2008) Privacy preserving crowd monitoring: counting people without people models or tracking. In: IEEE conference on computer vision and pattern recognition, 2008. CVPR 2008. IEEE, pp 1–7

  9. Chang T, Kuo C-C (1993) Texture analysis and classification with tree-structured wavelet transform. IEEE Trans Image Process 2(4):429–441

    Article  Google Scholar 

  10. Chen K, Loy CC, Gong S, Xiang T (2012) Feature mining for localised crowd counting. In: BMVC, vol 1. p 3

  11. Chen K, Gong S, Xiang T, Change Loy C (2013) Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2467–2474

  12. Chen S, Fern A, Todorovic S (2015) Person count localization in videos from noisy foreground and detections. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1364–1372

  13. Cho S-Y, Chow TW, Leung C-T (1999) A neural-based crowd estimation by hybrid global learning algorithm. IEEE Trans Syst Man Cybern B Cybern 29(4):535–541

    Article  Google Scholar 

  14. Cross GR, Jain AK (1983) Markov random field texture models. IEEE Trans Pattern Anal Mach Intell 1:25–39

    Article  Google Scholar 

  15. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE computer society conference on computer vision and pattern recognition, 2005. CVPR 2005, vol 1. IEEE, pp 886–893

  16. Davies AC, Yin JH, Velastin SA (1995) Crowd monitoring using image processing. Electronics & Communication Engineering Journal 7(1):37–47

    Article  Google Scholar 

  17. Dollar P, Wojek C, Schiele B, Perona P (2012) Pedestrian detection: an evaluation of the state of the art. IEEE Trans Pattern Anal Mach Intell 34(4):743–761

    Article  Google Scholar 

  18. Felzenszwalb P, McAllester D, Ramanan D (2008) A discriminatively trained, multiscale, deformable part model. In: IEEE conference on computer vision and pattern recognition, 2008. CVPR 2008. IEEE, pp 1–8

  19. Felzenszwalb PF, Girshick RB, McAllester D, Ramanan D (2010) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Mach Intell 32(9):1627–1645

    Article  Google Scholar 

  20. Fu M, Xu P, Li X, Liu Q, Ye M, Zhu C (2015) Fast crowd density estimation with convolutional neural networks. Eng Appl Artif Intell 43:81–88

    Article  Google Scholar 

  21. Ge W, Collins RT (2009) Marked point processes for crowd counting. In: IEEE conference on computer vision and pattern recognition, 2009. CVPR 2009. IEEE, pp 2913–2920

  22. Hussain N, Yatim HSM, Hussain NL, Yan JLS, Haron F (2011) Cdes: a pixel-based crowd density estimation system for masjid al-haram. Saf Sci 49(6):824–833

    Article  Google Scholar 

  23. Idrees H, Saleemi I, Seibert C, Shah M (2013) Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2547–2554

  24. Kang D, Ma Z, Chan AB (2017) Beyond counting: comparisons of density maps for crowd analysis tasks-counting, detection, and tracking. arXiv:1705.10118

  25. Kratz L, Nishino K (2009) Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models. In: IEEE conference on computer vision and pattern recognition, 2009. CVPR 2009. IEEE, pp 1446–1453

  26. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  27. Lamba S, Nain N (2017) Crowd monitoring and classification: a survey. In: Advances in computer and computational sciences. Springer, pp 21–31

  28. Lamba S, Nain N (2017) Multi-source approach for crowd density estimation in still images. In: 2017 IEEE international conference on identity, security and behavior analysis (ISBA). IEEE, pp 1–6

  29. Lamba S, Nain N, Chahar H (2016) A robust multi-model approach for face detection in crowd. In: 2016 12th international conference on signal-image technology & internet-based systems (SITIS). IEEE, pp 96–103

  30. Lempitsky V, Zisserman A (2010) Learning to count objects in images. In: Advances in neural information processing systems, pp 1324–1332

  31. Leung T, Malik J (1999) Recognizing surfaces using three-dimensional textons. In: The proceedings of the seventh IEEE international conference on computer vision, 1999, vol 2. IEEE, pp 1010–1017

  32. Li M, Zhang Z, Huang K, Tan T (2008) Estimating the number of people in crowded scenes by mid based foreground segmentation and head-shoulder detection. In: 19th international conference on pattern recognition, 2008. ICPR 2008. IEEE, pp 1–4

  33. Lin S-F, Chen J-Y, Chao H-X (2001) Estimation of number of people in crowded scenes using perspective transformation. IEEE Trans Syst Man Cybern Part A Syst Humans 31(6):645–654

    Article  Google Scholar 

  34. Ma R, Li L, Huang W, Tian Q (2004) On pixel count based crowd density estimation for visual surveillance. In: 2004 IEEE conference on cybernetics and intelligent systems, vol 1. IEEE, pp 170–173

  35. Ma W, Huang L, Liu C (2010) Crowd density analysis using co-occurrence texture features. In: 2010 5th international conference on computer sciences and convergence information technology (ICCIT). IEEE, pp 170–175

  36. Marana A, Costa LDF, Lotufo R, Velastin S (1998a) On the efficacy of texture analysis for crowd monitoring. In: International symposium on computer graphics, image processing, and vision, 1998. Proceedings. SIBGRAPI’98.IEEE , pp 354–361

  37. Marana AN, Velastin SA, Costa LDF, Lotufo R (1998b) Automatic estimation of crowd density using texture. Saf Sci 28(3):165–175

    Article  Google Scholar 

  38. Marana AN, Costa LDF, Lotufo R, Velastin SA (1999) Estimating crowd density with minkowski fractal dimension. In: 1999 IEEE international conference on acoustics, speech, and signal processing, 1999. Proceedings, vol 6. IEEE, pp 3521–3524

  39. Marsden M, McGuiness K, Little S, O’Connor NE (2016) Fully convolutional crowd counting on highly congested scenes. arXiv:1612.00220

  40. Miraftabzadeh SA, Rad P, Choo KKR, Jamshidi M (2017) A privacy-aware architecture at the edge for autonomous real-time identity re-identification in crowds. IEEE Internet of Things Journal. [In press]

  41. Rabaud V, Belongie S (2006) Counting crowded moving objects. In: 2006 IEEE computer society conference on computer vision and pattern recognition, vol 1. IEEE, pp 705–711

  42. Rodriguez M, Laptev I, Sivic J, Audibert J-Y (2011) Density-aware person detection and tracking in crowds. In: 2011 IEEE international conference on computer vision (ICCV). IEEE, pp 2423–2430

  43. Ryan D, Denman S, Fookes C, Sridharan S (2009) Crowd counting using multiple local features. In: Digital image computing: techniques and applications, 2009. DICTA’09. IEEE, pp 81–88

  44. Sabzmeydani P, Mori G (2007) Detecting pedestrians by learning shapelet features. In: IEEE conference on computer vision and pattern recognition, 2007. CVPR’07. IEEE, pp 1–8

  45. Srinivasan GN, Shobha G (2008) Statistical texture analysis. In: World academy of science, engineering and technology, vol 36. pp 1264–1269

  46. Vedaldi A, Fulkerson B (2010) Vlfeat: an open and portable library of computer vision algorithms. In: Proceedings of the 18th ACM international conference on multimedia. ACM, pp 1469–1472

  47. Viola P, Jones MJ (2004) Robust real-time face detection. Int J Comput Vis 57(2):137–154

    Article  Google Scholar 

  48. Wang X, Ma X, Grimson E (2007) Unsupervised activity perception by hierarchical bayesian models. In: IEEE conference on computer vision and pattern recognition, 2007. CVPR’07. IEEE, pp 1–8

  49. Wang C, Zhang H, Yang L, Liu S, Cao X (2015) Deep people counting in extremely dense crowds. In: Proceedings of the 23rd ACM international conference on multimedia. ACM, pp 1299–1302

  50. Wu B, Nevatia R (2005) Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors. In: Tenth IEEE international conference on computer vision, 2005. ICCV 2005, vol 1. IEEE, pp 90–97

  51. Wu B, Nevatia R (2007) Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. Int J Comput Vis 75 (2):247–266

    Article  Google Scholar 

  52. Xiaohua L, Lansun S, Huanqin L (2006) Estimation of crowd density based on wavelet and support vector machine. Trans Inst Meas Control 28(3):299–308

    Article  Google Scholar 

  53. Xu Z, Zhang H, Hu C, Mei L, Xuan J, Choo KKR, Sugumaran V, Zhu Y (2016) Building knowledge base of urban emergency events based on crowdsourcing of social media. Concurrency and Computation: Practice and Experience 28(15):4038–4052

    Article  Google Scholar 

  54. Zhang C, Li H, Wang X, Yang X (2015) Cross-scene crowd counting via deep convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 833–841

  55. Zhang Y, Zhou D, Chen S, Gao S, Ma Y (2016) Single-image crowd counting via multi-column convolutional neural network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 589–597

  56. Zhu S-C, Guo C-E, Wu Y, Wang Y (2002) What are textons?. In: European conference on computer vision. Springer, pp 793–807

Download references

Author information

Authors and Affiliations

  1. Malaviya National Institute of Technology, Jaipur, India

    Sonu Lamba & Neeta Nain

Authors
  1. Sonu Lamba
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Neeta Nain
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sonu Lamba.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lamba, S., Nain, N. A texture based mani-fold approach for crowd density estimation using Gaussian Markov Random Field. Multimed Tools Appl 78, 5645–5664 (2019). https://doi.org/10.1007/s11042-017-5554-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-017-5554-4

Keywords

Search

Navigation