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Single-Image Crowd Counting using Multi-Column Neural Network

by Rinku Mahesh Sharma
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 175 - Number 11
Year of Publication: 2020
Authors: Rinku Mahesh Sharma
10.5120/ijca2020920598

Rinku Mahesh Sharma . Single-Image Crowd Counting using Multi-Column Neural Network. International Journal of Computer Applications. 175, 11 ( Aug 2020), 31-35. DOI=10.5120/ijca2020920598

@article{ 10.5120/ijca2020920598,
author = { Rinku Mahesh Sharma },
title = { Single-Image Crowd Counting using Multi-Column Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2020 },
volume = { 175 },
number = { 11 },
month = { Aug },
year = { 2020 },
issn = { 0975-8887 },
pages = { 31-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume175/number11/31499-2020920598/ },
doi = { 10.5120/ijca2020920598 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:24:46.868561+05:30
%A Rinku Mahesh Sharma
%T Single-Image Crowd Counting using Multi-Column Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 175
%N 11
%P 31-35
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Crowd scene understanding is an important and challenging problem in computer vision. Most of studies based on tracking individuals, crowd counting, finding region of motion and alarming crowd flaws have came into existence. The task of crowd counting and density map estimation is riddled with many challenges such as occlusions, non-uniform density, intra-scene and inter-scene variations in scale and perspective. Nevertheless, over the last few years, crowd count analysis has evolved from earlier methods that are often limited to small variations in crowd density and scales to the current state-of-the-art methods that have developed the ability to perform successfully on a wide range of scenarios. The success of crowd counting methods in the recent years can be largely attributed to deep learning and publications of challenging datasets. One of the appropriate method that can accurately estimate the crowd count from an image with arbitrary crowd density and arbitrary perspective is using the state-of-the-art i.e. convolution neural network. The technique used for the crowd detection and crowd density estimation is through the Multicolumn Convolution Neural Network architecture. The model allows the input image to be of arbitrary size or resolution with high accuracy and produces a state of art results. The proposed work is implemented with the Shanghaitech dataset, which is among the largest dataset. The model produces highly precise and accurate results with the estimate crowd count and density map.

References
  1. Y. Zhang, D. Zhou, S. Chen, S. Gao, and Y. Ma, “Single-image crowd counting via multi-column convolutional neural network,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 589–597, 2016.
  2. M. J. Jones and D. Snow, “Pedestrian detection using boosted features over many frames,” International Conference on Pattern Recognition, 2008. ICPR 2008. IEEE, pp. 1–4, December 2008.
  3. P. Viola, M. J. Jones, and D. Snow, “Detecting pedestrians using patterns of motion and appearance,” International Journal of Computer Vision, vol. 63, no. 2, pp. 153–161, 2005.
  4. S. F. Lin, J. Y. Chen, and H. X. Chao, “Estimation of number of people in crowded scenes using perspective transformation,” IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol. 31, no. 6, pp. 645–654, 2001.
  5. “22 dead, several injured in stampede at mumbai’selphinstone road station, The Hindu,” https://www.thehindu.com/news/cities/mumbai/mumbai-stampede/ article19775073.ece, [Accessed Oct. 22, 2017].
  6. T. Zhao and R. Nevatia, “Tracking multiple humans in complex situations,” IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 26, no. 9, pp. 1208–1221, 2006.
  7. V. A. Sindagi and V. M. Patel, “A survey of recent advances in cnn-based single image crowd counting and density estimation,” Pattern Recognition Letters, Elsevier, vol. 107, pp. 1–16, 2017. 53
  8. S. Abdulla, M. Saleh, S. A. Suandi, and H. Ibrahim, “Recent survey on crowd density estimation and counting for visual surveillance,” Engineering Application of Artificial Intelligence, ScienceDirect, vol. 41, pp. 103–114, 2015.
  9. D. Helbing, D. Brockmann, T. Chadefaux, K. Donnay, U. Blanke, O. Woolley- Meza, M. Moussaid, A. Johansson, J. Krause, and S. Schutte, “Saving human lives: what complexity science and information systems can contribute,” Journal of Statistical Physics, vol. 158, no. 3, pp. 1–47, 2014.
  10. T. Zhao, R. Nevatia, and B. Wu, “Segmentation and tracking of multiple humans in crowded environments,” IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 13, no. 7, pp. 1198–1211, 2008.
  11. Death toll in MP stampede reaches 115; congress wants CM to quit, The Times of India,” https://timesofindia.indiatimes.com/india/ Death-toll-in-MP-stampede-reaches-115-Congress-wants-CM-to-quit/ articles how/24151591.cms, [Accessed Oct. 22, 2017].
  12. Kumbh mela chief azam khan resigns over stampede, BBC News,” https:// www.bbc.com/news/world-asia-india-21406879, [Accessed Oct. 22, 2017].
  13. R. E. Schapire and Y. Singer, “Improved boosting algorithms using confidence rated predictions,” Machine Learning, vol. 37, no. 3, pp. 297–336, 1999.
  14. B. Leibe, E. Seemann, and B. Schiele, “Pedestrian detection in crowded scenes,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 878–885, June 2005.
  15. S. F. Lin and C. D. Lin, “Estimation of the pedestrians on a crosswalk,” International
  16. Joint Conference SICE-ICASE, 2006. IEEE, pp. 4931–4936, October 2006.
  17. V. Rabaud and S. Belongie, “Counting crowded moving objects,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1. IEEE, pp. 705–711, June 2006.
  18. J. Shi and C. Tomasi, “Good features to track,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 593–600, June 1994.
  19. O. Sidla, Y. Lypetskyy, N. Brandle, and S. Seer, “Pedestrian detection and tracking for counting applications in crowded situations,” IEEE International Conference on Video and Signal Based Surveillance, November 2006.
  20. H. Idrees, I. Saleemi, C. Seibert, and M. Shah, “Multi-source multiscale counting in extremely dense crowd images,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2547–2554, 2013.
  21. C.Wang, H. Zhang, L. Yang, S. Liu, and X. Cao, “Deep people counting in extremely dense crowds,” Proceedings of the 23rd ACM international conference on Multimedia, ACM, pp. 1299–1302, 2015.
  22. P. Viola and M. J. Jones, “Robust real-time face detection,” International journal of computer vision, vol. 57, no. 2, pp. 137–154, 2004.
  23. M. Fu, P. Xu, X. Li, Q. Liu, M. Ye, and C. Zhu, “Fast crowd density estimation with convolutional neural networks,” Engineering Applications of Artificial Intelligence, vol. 43, pp. 81–88, 2015.
  24. L. Zeng, X. Xu, B. Cai, S. Qiu, and T. Zhang, “Multi-scale convolutional neural networks for crowd counting,” IEEE International Conference on Image Processing (ICIP), pp. 465–469, February 2017.
  25. D. Kang, Z. Ma, and A. B. Chan, “Beyond counting: Comparisons of density maps for crowd analysis tasks-counting, detection, and tracking,” Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–14, 2018.
  26. Bansal and K. S. Venkatesh, “People counting in high density crowds from still images,” International Journal of Computer and Electrical Engineering, vol. 7, no. 5, pp. 316–324, 2017.
  27. Dertat, “Applied deep learning - part 4: Convolutional neural networks,” https://towardsdatascience.com/ applied-deep-learning-part-4-convolutional-neural-networks-584bc134c1e2, [Accessed May 17, 2018].
  28. “Microsoft - cognitive toolkit - tutorial 2,” https://docs.microsoft.com/en-us/ cognitive-toolkit/tutorial2/tutorial2, [Accessed May 23, 2018].
  29. U. Udofia, “Basic overview of convolutional neural network (cnn),” https://medium.com/@udemeudofia01/ basic-overview-of-convolutional-neural-network-cnn-4fcc7dbb4f17, [Accessed May 24, 2018].
  30. “Deep learning for computer vision,” https://www.slideshare.net/Tricode/ deep-learning-stm-6, [Accessed May 24, 2018].
  31. S. Setty, M. Husain, P. Beham, J. Gudavalli, M. Kandasamy, R. Vaddi, V. Hemadri, J. C. Karure, R. Raju, Rajan, K. V., and J. C. V., “Indian Movie Face Database: A Benchmark for Face Recognition Under Wide Variations,” National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), pp. 1–5, December 2013.
  32. E. Osuna, R. Freund, and F. Girosit, “Training support vector machines: an application to face detection,” Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 130–136, 1997.
  33. C. Zhang, H. Li, X. Wang, and X. Yang, “Cross-scene crowd counting via deep convolutional neural networks,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 833–841, 2015.
  34. M. Mathias, R. Benenson, M. Pedersoli, and V. L. Gool, “Face detection without bells and whistles,” European conference on computer vision, pp. 720–735, 2014.
Index Terms

Computer Science
Information Sciences

Keywords

Deep learning Convolution neural network Crowd density Multicolumn Convolution Neural Network.

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