CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Machine Learning Techniques for Crowd Counting: A Survey

by Dakshi Chavan, Anuradha Purohit
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 37
Year of Publication: 2023
Authors: Dakshi Chavan, Anuradha Purohit
10.5120/ijca2023923176

Dakshi Chavan, Anuradha Purohit . Machine Learning Techniques for Crowd Counting: A Survey. International Journal of Computer Applications. 185, 37 ( Oct 2023), 1-8. DOI=10.5120/ijca2023923176

@article{ 10.5120/ijca2023923176,
author = { Dakshi Chavan, Anuradha Purohit },
title = { Machine Learning Techniques for Crowd Counting: A Survey },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2023 },
volume = { 185 },
number = { 37 },
month = { Oct },
year = { 2023 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number37/32928-2023923176/ },
doi = { 10.5120/ijca2023923176 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:27:59.883756+05:30
%A Dakshi Chavan
%A Anuradha Purohit
%T Machine Learning Techniques for Crowd Counting: A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 37
%P 1-8
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Crowd Counting process estimates the number of people in an image or video. It is a significant area of computer vision research that has numerous applications in crowd management, class student attendance management, temple crowd management, event planning, urban development, security and retail analytic and many more. Due to the increasing interest to provide efficient crowd management and public safety, several researchers have proposed methods based on detection, regression and density. This has made it feasible for both machine learning (ML) and deep learning (DL) approaches to deal with challenges to get accurate crowd counts. Machine learning and deep learning based models identify complex patterns, obtained increased accuracy and adjust to changing environmental conditions efficiently. In this paper, a survey on work done in crowd counting using machine learning techniques has been presented. The advantages and disadvantages of each approach has been discussed in detail.

References
  1. Deepak Babu Sam, Skand Vishwanath Peri, Mukuntha Narayanan Sundararaman, Amogh Kamath, and Venkatesh Babu Radhakrishnan. Locate, Size and Count: Accurately Resolving People in Dense Crowds via Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1–1, 2020.
  2. Saleh Basalamah, Sultan Daud Khan, and Habib Ullah. Scale Driven Convolutional Neural Network Model for People Counting and Localization in Crowd Scenes. IEEE Access, 7:71576–71584, 2019.
  3. Antoni B. Chan, Zhang-Sheng John Liang, and Nuno Vasconcelos. Privacy preserving crowd monitoring: Counting people without people models or tracking. In 2008 IEEE Conference on Computer Vision and Pattern Recognition, pages 1–7, June 2008. ISSN: 1063-6919.
  4. Antoni B. Chan and Nuno Vasconcelos. Bayesian Poisson regression for crowd counting. In 2009 IEEE 12th International Conference on Computer Vision, pages 545–551, September 2009. ISSN: 2380-7504.
  5. Ke Chen, Shaogang Gong, Tao Xiang, and Chen Change Loy. Cumulative Attribute Space for Age and Crowd Density Estimation. In 2013 IEEE Conference on Computer Vision and Pattern Recognition, pages 2467–2474, June 2013. ISSN: 1063-6919.
  6. N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), volume 1, pages 886–893, San Diego, CA, USA, 2005. IEEE.
  7. M. Enzweiler and D.M. Gavrila. Monocular Pedestrian Detection: Survey and Experiments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(12):2179–2195, December 2009.
  8. P F Felzenszwalb, R B Girshick, D McAllester, and D Ramanan. Object Detection with Discriminatively Trained Part-Based Models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9):1627–1645, September 2010.
  9. J. Gall, A. Yao, N. Razavi, L. Van Gool, and V. Lempitsky. Hough Forests for Object Detection, Tracking, and Action Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(11):2188–2202, November 2011.
  10. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770–778, Las Vegas, NV, USA, June 2016. IEEE.
  11. Mohammad Hossain, Mehrdad Hosseinzadeh, Omit Chanda, and Yang Wang. Crowd Counting Using Scale-Aware Attention Networks. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 1280–1288, Waikoloa Village, HI, USA, January 2019. IEEE.
  12. Ya-Li Hou and Grantham K. H. Pang. People Counting and Human Detection in a Challenging Situation. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 41(1):24–33, January 2011.
  13. Haroon Idrees, Imran Saleemi, Cody Seibert, and Mubarak Shah. Multi-source Multi-scale Counting in Extremely Dense Crowd Images. In 2013 IEEE Conference on Computer Vision and Pattern Recognition, pages 2547–2554, June 2013. ISSN: 1063-6919.
  14. Xiaoheng Jiang, Li Zhang, Tianzhu Zhang, Pei Lv, Bing Zhou, Yanwei Pang, Mingliang Xu, and Changsheng Xu. Density-Aware Multi-Task Learning for Crowd Counting. IEEE Transactions on Multimedia, 23:443–453, 2021.
  15. Xiaolong Jiang, Zehao Xiao, Baochang Zhang, Xiantong Zhen, Xianbin Cao, David Doermann, and Ling Shao. Crowd Counting and Density Estimation by Trellis Encoder-Decoder Networks. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 6126–6135, Long Beach, CA, USA, June 2019. IEEE.
  16. B. Leibe, E. Seemann, and B. Schiele. Pedestrian Detection in Crowded Scenes. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), volume 1, pages 878–885, San Diego, CA, USA, 2005. IEEE.
  17. Victor Lempitsky and Andrew Zisserman. Learning To Count Objects in Images. In Advances in Neural Information Processing Systems, volume 23. Curran Associates, Inc., 2010.
  18. Bo Li, Hongbo Huang, Ang Zhang, Peiwen Liu, and Cheng Liu. Approaches on crowd counting and density estimation: a review. Pattern Analysis and Applications, 24(3):853–874, August 2021.
  19. Min Li, Zhaoxiang Zhang, Kaiqi Huang, and Tieniu Tan. Estimating the number of people in crowded scenes by MID based foreground segmentation and head-shoulder detection. In 2008 19th International Conference on Pattern Recognition, pages 1–4, December 2008. ISSN: 1051-4651.
  20. Zhiheng Ma, Xing Wei, Xiaopeng Hong, and Yihong Gong. Bayesian Loss for Crowd Count Estimation With Point Supervision. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pages 6141–6150, Seoul, Korea (South), October 2019. IEEE.
  21. Ramin Mehran, Alexis Oyama, and Mubarak Shah. Abnormal crowd behavior detection using social force model. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pages 935–942, Miami, FL, June 2009. IEEE.
  22. David Ryan, Simon Denman, Clinton Fookes, and Sridha Sridharan. Crowd Counting Using Multiple Local Features. In 2009 Digital Image Computing: Techniques and Applications, pages 81–88, December 2009.
  23. Payam Sabzmeydani and Greg Mori. Detecting Pedestrians by Learning Shapelet Features. In 2007 IEEE Conference on Computer Vision and Pattern Recognition, pages 1–8, June 2007. ISSN: 1063-6919.
  24. Deepak Babu Sam, Shiv Surya, and R. Venkatesh Babu. Switching Convolutional Neural Network for Crowd Counting. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4031–4039, Honolulu, HI, July 2017. IEEE.
  25. Mamoona Birkhez Shami, Salman Maqbool, Hasan Sajid, Yasar Ayaz, and Sen-Ching Samson Cheung. People Counting in Dense Crowd Images Using Sparse Head Detections. IEEE Transactions on Circuits and Systems for Video Technology, 29(9):2627–2636, September 2019.
  26. Sheng-Fuu Lin, Jaw-Yeh Chen, and Hung-Xin Chao. Estimation of number of people in crowded scenes using perspective transformation. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 31(6):645–654, November 2001.
  27. Vishwanath A. Sindagi and Vishal M. Patel. A survey of recent advances in CNN-based single image crowd counting and density estimation. Pattern Recognition Letters, 107:3–16, May 2018.
  28. C. Stauffer and W.E.L. Grimson. Adaptive background mixture models for real-time tracking. In Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), pages 246–252, Fort Collins, CO, USA, 1999. IEEE Comput. Soc.
  29. Venkatesh Bala Subburaman, Adrien Descamps, and Cyril Carincotte. Counting People in the Crowd Using a Generic Head Detector. In 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance, pages 470–475, September 2012.
  30. Yukun Tian, Yiming Lei, Junping Zhang, and James Z.Wang. PaDNet: Pan-Density Crowd Counting. IEEE Transactions on Image Processing, 29:2714–2727, 2020.
  31. O. Tuzel, F. Porikli, and P. Meer. Pedestrian Detection via Classification on Riemannian Manifolds. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(10):1713–1727, October 2008.
  32. Viola, Jones, and Snow. Detecting pedestrians using patterns of motion and appearance. In Proceedings Ninth IEEE International Conference on Computer Vision, pages 734–741 vol.2, October 2003.
  33. P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, volume 1, pages I–511–I–518, Kauai, HI, USA, 2001. IEEE Comput. Soc.
  34. Paul Viola and Michael J. Jones. Robust Real-Time Face Detection. International Journal of Computer Vision, 57(2):137–154, May 2004.
  35. Boyu Wang, Huidong Liu, Dimitris Samaras, and Minh Hoai Nguyen. Distribution Matching for Crowd Counting. In Advances in Neural Information Processing Systems, volume 33, pages 1595–1607. Curran Associates, Inc., 2020.
  36. Bo Wu and R. Nevatia. 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 (ICCV’05) Volume 1, volume 1, pages 90–97 Vol. 1, October 2005. ISSN: 2380-7504.
  37. BoWu and Ram Nevatia. Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet based Part Detectors. International Journal of Computer Vision, 75(2):247–266, August 2007.
  38. Zhaoyi Yan, Yuchen Yuan,Wangmeng Zuo, Xiao Tan, Yezhen Wang, Shilei Wen, and Errui Ding. Perspective-Guided Convolution Networks for Crowd Counting. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pages 952–961, Seoul, Korea (South), October 2019. IEEE.
  39. Yifan Yang, Guorong Li, Zhe Wu, Li Su, Qingming Huang, and Nicu Sebe. Reverse Perspective Network for Perspective-Aware Object Counting. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 4373–4382, Seattle, WA, USA, June 2020. IEEE.
  40. Anran Zhang, Jun Xu, Xiaoyan Luo, Xianbin Cao, and Xiantong Zhen. Cross-Domain Attention Network for Unsupervised Domain Adaptation Crowd Counting. IEEE Transactions on Circuits and Systems for Video Technology, 32(10):6686–6699, October 2022.
  41. Cong Zhang, Hongsheng Li, Xiaogang Wang, and Xiaokang Yang. Cross-Scene Crowd Counting via Deep Convolutional Neural Networks. pages 833–841, 2015.
  42. Yingying Zhang, Desen Zhou, Siqin Chen, Shenghua Gao, and Yi Ma. Single-Image Crowd Counting via Multi-Column Convolutional Neural Network. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 589–597, Las Vegas, NV, USA, June 2016. IEEE.
Index Terms

Computer Science
Information Sciences

Keywords

Crowd counting Crowd management Machine learning (ML) Deep learning (DL) Detection

Powered by PhDFocusTM