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Crowd Counting for Optimal Resource Management

Shubham K. Darak, Atharva R. Chavan, Sanjivani S. Pande and Renuka L. Brahme

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Darak, S.K., Chavan, A.R., Pande, S.S. and Brahme, R.L., 2021. Crowd Counting for Optimal Resource Management. United International Journal for Research & Technology (UIJRT), 2(7), pp.01-04.


The aim of crowd counting using optimal resource management is to estimate the number of people in crowded images or videos from surveillance cameras so that, respective authorities can get effective analysis of crowd flow and can effectively manage resources. Calculating number of people from various images from digital cameras or videos has variety of applications such as traffic monitoring, foot traffic counting from retail stores, safety applications and counting at tremendous crowd locations like in Masjid-e-Haram during Hajj and Umrah congregation and to develop strategy to manage the crowd in most optimal way. In addition to this we can have applications of crowd counting in various day-to-day applications like counting for some survey purposes. So Crowd Counting provides foot traffic at places such as Malls, Retail Stores and Public streets for every moment of time. This count will be used to provide statistical flow of crowd based on day, week, month and year at respective place. The crowd counting has some challenges too like non-uniform density images, background noises and occlusions present in images. Nevertheless, lot of research has been done in recent past and many new methodologies are evolving which are dealing really effectively with stated problems. In this paper, we are doing comparative study of various methodologies which are used for crowd counting and we are providing comprehensive idea about Convolutional Neural Network based approaches such as Multi Scale Convolutional Neural Network.

Keywords: Multi Scale Convolutional Neural Network, Convolutional Neural Network, Deep Learning, Crowd Counting, Image Processing.


  1. Fu, M., Xu, P., Li, X., Liu, Q., Ye, M., Zhu, C., 2015. Fast crowd density estimation with convolutional neural networks. Engineering Applications of Artificial Intelligence 43, 81–88.
  2. 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.
  3. 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.
  4. Walach, E., Wolf, L., 2016. Learning to count with cnn boosting, in: European Conference on Computer Vision, Springer. pp. 660–676.
  5. Shang, C., Ai, H., Bai, B., 2016. End-to-end crowd counting via joint learning local and global count, in: Image Processing (ICIP), 2016 IEEE International Conference on, IEEE. pp. 1215–1219.
  6. Krizhevsky, A., Sutskever, I., Hinton, G.E., 2012. Imagenet classification with deep convolutional neural networks, in: Advances in neural information processing systems, pp. 1097–1105.

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