Skip to main content

Advertisement

SpringerLink
Log in

Crowd analytics: literature and technological assessment

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

Abstract

In previous years, the world has gone through several natural disasters like Tsunami, earthquakes, floods, tornadoes, hurricanes, cyclones, etc., and manufactured disasters such as stampedes, fire, terror attacks, etc. A large number of causalities are reported, with a massive loss to life, economy, and other things. Knowing this, we should make a transit from a reliable and flexible disaster management approach to a proactive one by leveraging advances in science and technology. A colossal increase in worldwide population points out that the occurrence of a crowd at any place is becoming more and more familiar with each passing day. It is undeniable that these mass gatherings often become a source of a crowd-related catastrophe such as sudden escape, terror attacks, mob lynching, human stampede, or human crushing. Prior research on the crowd’s social, psychological, and computational dynamics has indicated that the crowd’s behavior under such devastating conditions is greatly decisive for crowd safety, its access or escape from the affected region, and emergency evacuation. Despite this, there is a certain paucity of pragmatic research on extreme crowd-related use cases and how to deal with such situations effectively. Through the past years, people and media have shared the details of such happenings and their experiences on a micro-level through various social network mediums. Attempts are being made to analyze this data using advanced technological tools and methods to extract the trends out of such happenings and predict any future happenings so that countermeasures can be taken and they can be prevented. This paper makes a structured literature assessment on the current scenario and systematically surveys the studies made in this field. It paves the path for future rendezvous in this area to unearth the hidden gold mine of information along the timeline. Also, an attempt is made to develop a technological solution or system that may help achieve an elevated level of social security via holistic video surveillance capable of detecting any crowd-related anomaly and proactively warning the concerned authorities about any such casualty. This will ensure that crowd disasters can be prevented well in time by gaining prior insights about them. The system is developed that encompasses everything from human detection, tracking, and counting to any abnormal behavior detection. The same has been achieved with 93.33% accuracy.

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
Fig. 8

Similar content being viewed by others

References

  1. Altamimi AB, Ullah H (2020) Panic detection in crowded scenes. Eng Technol Appl Sc Res 10(2):5412–5418

    Article  Google Scholar 

  2. Amarilli A, Amsterdamer Y, Milo T (2014) Uncertainty in crowd data sourcing under structural constraints. In: Database Systems for Advanced Applications. Springer, Berlin Heidelberg, pp 351–359

    Google Scholar 

  3. Sirine Ammar, Thierry Bouwmans, Nizar Zaghden, Mahmoud Neji Laboratoire MIRACL, Université de Sfax, Sfax, Tunisie Laboratoire MIA, Université de La Rochelle, Avenue M. Crépeau, 17000 La Rochelle, France 3ESC, Université de Sfax, Sfax, Tunisie (n.d.) A Deep Detector Classifier (DeepDC) for moving objects segmentation and classification in video surveillance

  4. Amsterdamer Y, Grossman Y, Milo T, & Senellart P (2013) “Crowd mining”. In Proceedings of the 2013 ACM SIGMOD international conference on Management of Data (pp. 241-252).

  5. Amsterdamer Y, Davidson SB, Milo T, Novgorodov S, & Somech A (2014) “OASSIS: query driven crowd mining”. In Proceedings of the 2014 ACM SIGMOD international conference on management of data (pp. 589-600).

  6. Amsterdamer Y, Davidson SB, Milo T, Novgorodov S, Somech A (2014) Ontology assisted crowd mining. Proceedings of the VLDB Endowment 7(13):15971600–15971600

    Article  Google Scholar 

  7. Amsterdamer Y, Kukliansky A, Milo T (2015) NL 2 CM: a natural language Interface to crowd mining. In: Proceedings of the 2015 ACM SIGMOD international conference on Management of Data, pp 1433–1438

    Chapter  Google Scholar 

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

  9. Biswas S, Praveen RG, Babu RV (2014) Super-pixel based crowd flow segmentation in H. 264 compressed videos. In: Image processing (ICIP), 2014 IEEE international conference on. IEEE, pp 2319–2323

    Chapter  Google Scholar 

  10. Butenuth M, Burkert F, Schmidt F, Hinz S, Hartmann D, Kneidl A, Sirmacek B (2011) Integrating pedestrian simulation, tracking and event detection for crowd analysis. In: Computer vision workshops (ICCV workshops), 2011 IEEE international conference on, pp 150–157

    Chapter  Google Scholar 

  11. Chaudhari MD, Ghotkar AS (2018) A study on crowd detection and density analysis for safety control. Int J Comput Sciences and Engineering. 6:424–428

    Article  Google Scholar 

  12. Chaudhary S, Khan MA, Bhatnagar C (2018) Multiple anomalous activity detection in videos. Proc Comput Sci 125:336–345

    Article  Google Scholar 

  13. Chen K, Loy CC, Gong S, Xiang T (2012) Feature mining for localised crowd counting. BMVC 1(2):3

  14. Davies C, Yin JH, Valestin SA (1995) Crowd monitoring using image processing. IEEE Electron Commun Eng J 7(1):37–47

    Article  Google Scholar 

  15. Goyal A et al (2020) Automatic border “surveillance using machine learning in remote video surveillance systems”. In: Hitendra Sarma T, Sankar V, Shaik R (eds) Emerging trends in electrical, communications, and information technologies. Lecture notes in electrical Engineering, vol 569. Springer, Singapore

    Google Scholar 

  16. Hao Y, Xu ZJ, Liu Y, Wang J, Fan JL (2019) Effective crowd anomaly detection through spatio-temporal texture analysis. Int J Autom Comput 16(1):27–39

    Article  Google Scholar 

  17. Feixiang He, Yuanhang Xiang and Xi Zhao & He Wang, (2020) “Informative scene decomposition for crowd analysis, comparison and simulation guidance”.

  18. Ilyas N, Shahzad A, Kim K (2020) Convolutional-neural network-based image crowd counting: review, categorization, analysis, and performance evaluation. Sensors 20:43

    Article  Google Scholar 

  19. Isella L, Stehlé J, Barrat A, Cattuto C, Pinton JF, Van den Broeck W (2011) What's in a crowd? Analysis of face-to-face behavioral networks. J Theor Biol 271(1):166–180

    Article  MathSciNet  Google Scholar 

  20. Ji H, Zeng X, Li H, Ding W, Nie X, Zhang Y, Xiao Z (2020) Human abnormal behavior detection method based on T-TINY-YOLO. In: Proceedings of the 5th international conference on multimedia and image processing, pp 1–5

    Google Scholar 

  21. Johansson A, Helbing D, Al-Abideen HZ, Al-Bosta S (2008) From crowd dynamics to crowd safety: a video-based analysis. Adv Complex Syst 11(04):497–527

    Article  Google Scholar 

  22. Junior JSJ, Musse S, Jung C (2010) Crowd analysis using computer vision techniques. IEEE Signal Process Mag 5(27):66–77

    Google Scholar 

  23. Kumar M, Bhatnagar C (2017) Crowd behavior recognition using hybrid tracking model and genetic algorithm enabled neural network. Int J Comput Intell Syst 10(1):234–246

    Article  Google Scholar 

  24. Li T, Chang H, Wang M, Ni B, Hong R, Yan S (2015) Crowded scene analysis: a survey. IEEE Trans Circ Syst Video Technol 25(3):367–386. https://doi.org/10.1109/TCSVT.2014.2358029

    Article  Google Scholar 

  25. Li X, Chen M, Wang Q (2020) Quantifying and detecting collective motion in crowd scenes. IEEE Trans Image Process 29:5571–5583. https://doi.org/10.1109/TIP.2020.2985284

    Article  Google Scholar 

  26. Liu CY, Liao WH, Ruan SJ (2018) Crowd gathering detection based on the foreground stillness model. IEICE Trans Inf Syst 101(7):1968–1971

    Article  Google Scholar 

  27. Ma J, Xu Y, Zhang Y, Jiang Y (2019) An abnormal behavior detection method of video crowds and vehicles based on deep learning. In: Proceedings of the 2nd international conference on artificial intelligence and pattern recognition, pp 10–12

    Chapter  Google Scholar 

  28. Nayan N, Sahu SS, Kumar S (2019) Detecting anomalous crowd behavior using correlation analysis of optical flow. SIViP:1–9

  29. Patel SP, Deshmukh SS, Rajbhar AR (2013) Geo location big data based collaborative crowd sourced data mining architecture for environmental monitoring and vegetation management systems. Int J Adv Res Comput Sci 4(3)

  30. Prasanna WG, Sumalini T (2015) Stampedes are community avertible crowd disasters” in second world conference on disaster management

  31. Rodriguez C, Daniel F, Casati F (2014) Crowd-based mining of reusable process model patterns. In: Business process management. Springer International Publishing, pp 51–66

    Chapter  Google Scholar 

  32. Rogstadius J, Kostakos V, Laredo J, Vukovic M (2011) Towards real-time emergency response using crowd supported analysis of social media. In: Proceedings of CHI workshop on crowdsourcing and human computation, systems, studies and platforms

    Google Scholar 

  33. Singh K, Rajora S, Vishwakarma DK, Tripathi G, Kumar S, Walia GS (2020) Crowd anomaly detection using aggregation of ensembles of fine-tuned ConvNets. Neurocomputing 371:188–198

    Article  Google Scholar 

  34. Solmaz B, Moore BE, Shah M (2012) Identifying behaviors in crowd scenes using stability analysis for dynamical systems. Pattern Analysis and Machine Intelligence, IEEE Transactions on 34(10):2064–2070

    Article  Google Scholar 

  35. Sreenu G, Durai MS (2019) Intelligent video surveillance: a review through deep learning techniques for crowd analysis. J Big Data 6(1):48

    Article  Google Scholar 

  36. Truong NB, Lee GM, Um T, Mackay M (2019) Trust evaluation mechanism for user recruitment in Mobile crowd-sensing in the internet of things. IEEE Transactions on Information Forensics and Security 14(10):2705–2719. https://doi.org/10.1109/TIFS.2019.2903659

    Article  Google Scholar 

  37. United States Patent Reddy et al (n.d.) Patent No.: US 10, 375, 150 B2 – Crowdbased device trust establishment in a connected environment

  38. Vashistha A, Vaish R, Cutrell E, Thies W (2015) The whodunit challenge: mobilizing the crowd in India. In: Human-computer interaction–INTERACT 2015. Springer International Publishing, pp 505–521

    Chapter  Google Scholar 

  39. Wang J, Xu Z (2015) Crowd anomaly detection for automated video surveillance. In: 6th international conference on imaging for crime prevention a detection. ICDP (15). IET, London, UK, p 4. isbn:978-1-78561-131-5

    Google Scholar 

  40. Wu X, Liang G, Lee KK, Xu Y (2006) Crowd density estimation using texture analysis and learning. In: Robotics and biomimetics, 2006. ROBIO'06. IEEE international conference on, pp 214–219

    Chapter  Google Scholar 

  41. Xintong G, Hongzhi W, Song Y, Hong G (2014) Brief survey of crowdsourcing for data mining. Expert Syst Appl 41(17):7987–7994

    Article  Google Scholar 

  42. Xu M, Hijazi I, Mebarki A, Meouche RE, Abune'meh M (2016) Indoor guided evacuation: TIN for graph generation and crowd evacuation. Geomatics, Natural Hazards and Risk:1–10

  43. Yang B, Cao J, Ni R, Zou L (2018) Anomaly detection in moving crowds through spatiotemporal autoencoding and additional attention. Adv Multimed

  44. Yogameena B, Priya KS (2014) Human crowd behavior analysis based on graph modelling and matching in synoptic video. Int J Innov Res Sci Eng Technol 3

  45. Zhan B, Monekosso DN, Remagnino P, Velastin SA, Xu LQ (2008) Crowd analysis: a survey. Mach Vis Appl 19(5–6):345–357

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of CSE&IT, Jaypee Institute of Information Technology, Noida, India

    Himani Bansal

  2. Department of CSE, Galgotias College of Engineering & Technology, Greater Noida, India

    Kavita Sharma

  3. School of Computer & System Sciences, Jawaharlal Nehru University, New Delhi, India

    Manju Khari

Authors
  1. Himani Bansal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kavita Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Manju Khari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Himani Bansal.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bansal, H., Sharma, K. & Khari, M. Crowd analytics: literature and technological assessment. Multimed Tools Appl 81, 15249–15283 (2022). https://doi.org/10.1007/s11042-022-12274-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-12274-8

Keywords

Search

Navigation