A Comparison of ML and DL Approaches for Crowd Analysis on the Hajj Pilgrimage-Reference-Cited by-同舟云学术

A Comparison of ML and DL Approaches for Crowd Analysis on the Hajj Pilgrimage

Published:2021 Issue: Volume: Page:552-561
ISSN:0302-9743
Container-title:Advances in Visual Informatics
language:
Short-container-title:

Author:

Zamri Muhammad Nur Hakim Bin^ORCID,Abdullah Junaidi^ORCID,Bhuiyan Roman^ORCID,Hashim Noramiza^ORCID,Al Farid Fahmid^ORCID,Uddin Jia^ORCID,Husen Mohd Nizam^ORCID,Abdullah Norra

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-90235-3_48

Reference16 articles.

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2. Li, B., Han, X., Wu, D.: Real-time crowd density estimation based on convolutional neural networks. In: Proceedings - 3rd International Conference Intelligent Transport Big Data Smart City, ICITBS 2018, vol. 2018-Janua, pp. 690–694 (2018). https://doi.org/10.1109/ICITBS.2018.00179

3. Ahuja, K.R., Charniya, N.N.: A survey of recent advances in crowd density estimation using image processing. In: Proceedings 4th International Conference Communications Electronic System, ICCES 2019, no. Icces, pp. 1207–1213 (2019). https://doi.org/10.1109/ICCES45898.2019.9002291

4. Pu, S., Song, T., Zhang, Y., Xie, D.: Estimation of crowd density in surveillance scenes based on deep convolutional neural network. Procedia Comput. Sci. 111, 154–159 (2017). https://doi.org/10.1016/j.procs.2017.06.022

5. Saleh, S.A.M., Suandi, S.A., Ibrahim, H.: Recent survey on crowd density estimation and counting for visual surveillance. Eng. Appl. Artif. Intell. 41, 103–114 (2015). https://doi.org/10.1016/j.engappai.2015.01.007