CFP last date
20 January 2025
Reseach Article

Survey of Crowd Detection Algorithms using Wireless Sensor Networks: A Case of People Crowds

by Obbo Aggrey, Nabaasa Evarist
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 38
Year of Publication: 2019
Authors: Obbo Aggrey, Nabaasa Evarist
10.5120/ijca2019918383

Obbo Aggrey, Nabaasa Evarist . Survey of Crowd Detection Algorithms using Wireless Sensor Networks: A Case of People Crowds. International Journal of Computer Applications. 182, 38 ( Jan 2019), 1-7. DOI=10.5120/ijca2019918383

@article{ 10.5120/ijca2019918383,
author = { Obbo Aggrey, Nabaasa Evarist },
title = { Survey of Crowd Detection Algorithms using Wireless Sensor Networks: A Case of People Crowds },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2019 },
volume = { 182 },
number = { 38 },
month = { Jan },
year = { 2019 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number38/30311-2019918383/ },
doi = { 10.5120/ijca2019918383 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:13:38.202998+05:30
%A Obbo Aggrey
%A Nabaasa Evarist
%T Survey of Crowd Detection Algorithms using Wireless Sensor Networks: A Case of People Crowds
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 38
%P 1-7
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper a survey is carried out on crowd detection algorithms, highlighting challenges and gaps vis-a-avis people crowd detection. The research identifies some of the key capabilities considered invaluable in effective people crowd detection and compares some of the current crowd detection algorithms with regard to these. The results reveal that most algorithms are primarily non-people crowd detection algorithms. While people crowds are intelligent and can easily bypass most of the current crowd detection algorithms. Given the need for detection and management of people crowds, there is great need for specialized algorithms for people crowd detection using effective and non resource intensive methods.

References
  1. Gaddafi Abdul-Salaam, Abdul Hanan Abdullah, Mohammad Hossein Anisi, Abdullah Gani, and Abdulhameed Alelaiwi. A comparative analysis of energy conservation approaches in hybrid wireless sensor networks data collection protocols. Telecommunication Systems, 61(1):159–179, 2016.
  2. Andrea Abrardo, Marco Martal`o, and Gianluigi Ferrari. Information fusion for efficient target detection in large-scale surveillance wireless sensor networks. Information Fusion, 38, 2017.
  3. Bader A Alyoubi and Ibrahiem MM El Emary. The zigbee wireless sensor netwrok in medical applications: A critical analysis study. Journal of Current Research in Science, 4(1):7, 2016.
  4. Claudia Arcidiacono, Simona MC Porto, Massimo Mancino, and Giovanni Cascone. A threshold-based algorithm for the development of inertial sensor-based systems to perform realtime cow step counting in free-stall barns. Biosystems Engineering, 153, 2017.
  5. Matthias Butenuth, Florian Burkert, Florian Schmidt, Stefan Hinz, Dirk Hartmann, Angelika Kneidl, Andr´e Borrmann, and Beril Sirmacek. Integrating pedestrian simulation, tracking and event detection for crowd analysis. In Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on, 2011.
  6. Saandeep Depatla, Arjun Muralidharan, and Yasamin Mostofi. Occupancy estimation using only wifi power measurements. IEEE Journal on Selected Areas in Communications, 33(7):1381–1393, 2015.
  7. Pierre Deville, Catherine Linard, Samuel Martin, Marius Gilbert, Forrest R Stevens, Andrea E Gaughan, Vincent D Blondel, and Andrew J Tatem. Dynamic population mapping using mobile phone data. Proceedings of the National Academy of Sciences, 111(45):15888–15893, 2014.
  8. Oxford English Dictionary.
  9. Qing Fang, Feng Zhao, and Leonidas Guibas. Counting targets: Building and managing aggregates in wireless sensor networks. Palo Alto Research Center Technical Report, 2002.
  10. Kang Han, Wanggen Wan, Haiyan Yao, and Li Hou. Image crowd counting using convolutional neural network and markov random field. arXiv preprint arXiv:1706.03686, 2017.
  11. Tian He, Pascal Vicaire, Ting Yan, Liqian Luo, Lin Gu, Gang Zhou, Radu Stoleru, Qing Cao, John A Stankovic, and Tarek Abdelzaher. Achieving real-time target tracking usingwireless sensor networks. In Real-Time and Embedded Technology and Applications Symposium, 2006. Proceedings of the 12th IEEE, volume 10298, pages 37–48. IEEE, 2006.
  12. Yaocong Hu, Huan Chang, Fudong Nian, Yan Wang, and Teng Li. Dense crowd counting from still images with convolutional neural networks. Journal of Visual Communication and Image Representation, 38, 2016.
  13. Changkun Jiang, Lin Gao, Lingjie Duan, and Jianwei Huang. Scalable mobile crowdsensing via peer-to-peer data sharing. arXiv preprint arXiv:1705.05343, 2017.
  14. Robin Kravets, Hilfi Alkaff, Andrew Campbell, Karrie Karahalios, and Klara Nahrstedt. Crowdwatch: enabling innetwork crowd-sourcing.
  15. Patrick Laube, Matt Duckham, and ThomasWolle. Decentralized movement pattern detection amongst mobile geosensor nodes. Geographic information science, 2008.
  16. Trista Lin, Herv´e Rivano, and Fr´ed´eric Le Mou¨el. A survey of smart parking solutions. IEEE Transactions on Intelligent Transportation Systems, 2017.
  17. Kin Sum Liu, Sirajum Munir, Jonathan Francis, Charles Shelton, and Shan Lin. Long term occupancy estimation in a commercial space: an empirical study. In IPSN, 2017.
  18. M Nakatsuka, H Iwatani, and J Katto. A study on passive crowd density estimation using wireless sensors. In The 4th Intl. Conf. on Mobile Computing and Ubiquitous Networking (ICMU 2008), 2008.
  19. Ngoc-Tu Nguyen, Bing-Hong Liu, Van-Trung Pham, and Yi- Sheng Luo. On maximizing the lifetime for data aggregation in wireless sensor networks using virtual data aggregation trees. Computer Networks, 105, 2016.
  20. Eric L Piza, Andrew M Gilchrist, Joel M Caplan, Leslie W Kennedy, and Brian A OHara. The financial implications of merging proactive cctv monitoring and directed police patrol: a cost–benefit analysis. Journal of Experimental Criminology, 12(3):403–429, 2016.
  21. Yordan P Raykov, Emre Ozer, Ganesh Dasika, Alexis Boukouvalas, and Max A Little. Predicting room occupancy with a single passive infrared (pir) sensor through behavior extraction. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2016.
  22. Fabio Ricciato, Peter Widhalm, Massimo Craglia, and Francesco Pantisano.
  23. Lorenz Schauer, MartinWerner, and PhilippMarcus. Estimating crowd densities and pedestrian flows using wi-fi and bluetooth. In Proceedings of the 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, 2014.
  24. Patrick Senti. Distributed People Counting Using a Wireless Sensor Network. PhD thesis.
  25. Erick Stattner, Martine Collard, Philippe Hunel, and Nicolas Vidot. Detecting movement patterns with wireless sensor networks: application to bird behavior. In Proceedings of the 8th International Conference on Advances in Mobile Computing and Multimedia, 2010.
  26. Robert Tomastik, Satish Narayanan, Andrzej Banaszuk, and Sean Meyn. Model-based real-time estimation of building occupancy during emergency egress. In Pedestrian and Evacuation Dynamics 2008. 2010.
  27. Zhen Tu, Kai Zhao, Fengli Xu, Yong Li, Li Su, and Depeng Jin. Beyond k-anonymity: protect your trajectory from semantic attack. In Sensing, Communication, and Networking (SECON), 2017 14th Annual IEEE International Conference on, pages 1–9. IEEE, 2017.
  28. Jiafu Wan, Jianqi Liu, Zehui Shao, Athanasios V Vasilakos, Muhammad Imran, and Keliang Zhou. Mobile crowd sensing for traffic prediction in internet of vehicles. Sensors, 16(1):88, 2016.
  29. Fengli Xu, Pengyu Zhang, and Yong Li. Context-aware realtime population estimation for metropolis. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pages 1064–1075. ACM, 2016.
  30. Moustafa Youssef, Matthew Mah, and Ashok Agrawala. Challenges: device-free passive localization for wireless environments. In Proceedings of the 13th annual ACM international conference on Mobile computing and networking, 2007.
  31. Yaoxuan Yuan, Chen Qiu, Wei Xi, and Jizhong Zhao. Crowd density estimation using wireless sensor networks. In Mobile Ad-hoc and Sensor Networks (MSN), 2011 Seventh International Conference on, 2011.
Index Terms

Computer Science
Information Sciences

Keywords

Crowd Detection Algorithms People Crowds Detection