Abstract
With the expanding population and several problems arising due to crowded situations, the necessity of crowd detection is also at a raise. It includes assessing the number of individuals in the group and in addition the appropriation of the crowd density in different regions of the group. Estimation of such crowd density can be done from the image of the crowded scene. This paper proposes real-time approaches to solving problems related to dense crowds. It uses static images captured from a crowded area and attempts to estimate the crowd density of the area by applying two different techniques. The proposed techniques for estimating crowd are via image processing and another one is using Convolutional Neural Network. In image processing technique, erosion is used to count head regions from the images. Images considered here are grayscale images. In CNN approach, deep learning technique is used to estimate the crowd density. A network of Raspberry Pi is used in order to perform the crowd detection as well as circulating the crowd condition to responsible authorities. At that point, the authorities might be alarmed about the likelihood of a stampede and can find a way to keep away from it.
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References
Tang NC, Lin Y-Y, Weng M-F, Liao H-YM (2015) Cross-camera knowledge transfer for multiview people counting. IEEE Trans Image Process 24(1):80–93
Liu B, Vasconcelos N (2015) Bayesian model adaptation for crowd counts. In: Proceedings of IEEE international conference on computer vision (ICCV), Santiago, pp 4175–4183
Ma HD, Zeng CB, Ling CX (2012) A reliable people counting system via multiple cameras. ACM Trans Intell Syst Technol 3(2):1–22
Antic B, Letic D, Culibrk D, Crnojevic V (2009) K- means based segmentation for real time zenithal people counting. In: Proceedings of IEEE international conference on image processing, pp 2565–2568
Shao J (2017) Crowded scene understanding by deeply learned volumetric slices. IEEE Trans Circuits Syst Video Technol 27(3):613–623
Bansal A, Venkatesh KS (2009) People counting in high density crowds from still images. In: Proceedings of. IEEE international conference on computer vision and pattern recognition, pp 1093–1100
Fu H, Ma H, Xiao H (2014a) Crowd counting via head detection and motion flow estimation. In: Proceedings of 22nd ACM international conference on Multimedia, Florida, pp 877–880
Fu H, Ma H, Xiao H (2014b) Real-time accurate crowd counting based on RGBD information. In: Proceedings of IEEE international conference on image processing, pp 2585–2568
Chauhan RV, Kumar S, Singh SK (2016) Human count estimation in high density crowd images and videos. In: Proceedings of fourth international conference on parallel, distributed and grid computing (PDGC), Waknaghat, pp 343–347
Brostow GJ, Cipolla R (2006) Unsupervised bayesian detection of independent motion in crowds. In: Proceedings IEEE computer society conference on computer vision and pattern recognition (CVPR’06), pp 594–601
Zhang C, Li H, Wang X, Yang X (2015) Cross-scene crowd counting via deep convolutional neural networks. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR), Boston, MA, pp 833–841
Sezgin M, Sankur B (2004) Survey over image thresholding techniques and quantative performance evaluation. J Electron Imaging 13(1):146–165
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105
Sun T, Wang Y, Yang J, Hu X (2017) Convolution neural networks with two pathways for image style recognition. IEEE Trans Image Process 26(9):4102–4113
Reddy K, Shah M (2012) Recognizing 50 human action categories of web videos. Mach Vis Appl 1–11
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Haque, S., Sadi, M.S., Rafi, M.E.H., Islam, M.M., Hasan, M.K. (2020). Real-Time Crowd Detection to Prevent Stampede. In: Uddin, M., Bansal, J. (eds) Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-13-7564-4_56
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DOI: https://doi.org/10.1007/978-981-13-7564-4_56
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