A comprehensive analysis for crowd counting methodologies and algorithms in Internet of Things-Reference-Cited by-同舟云学术

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A comprehensive analysis for crowd counting methodologies and algorithms in Internet of Things

Published:2023-03-09 Issue: Volume: Page:
ISSN:1386-7857
Container-title:Cluster Computing
language:en
Short-container-title:Cluster Comput

Author:

Gao Mingliang,Souri Alireza,Zaker Mayram,Zhai Wenzhe,Guo Xiangyu,Li Qilei

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Software

Link

https://link.springer.com/content/pdf/10.1007/s10586-023-03987-y.pdf

Reference62 articles.

1. Al-Zaydi, Z., Vuksanovic, B., Habeeb, I.: Image processing based ambient context-aware people detection and counting. Int. J. Mach. Learn. Comput. 8(3), 268–273 (2018)

2. Almalki, K.J., Choi, B.Y., Chen, Y., Song, S.: Characterizing scattered occlusions for effective dense-mode crowd counting. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3840–3849 (2021)

3. Amirgholipour, S., Jia, W., Liu, L., Fan, X., Wang, D., He, X.: Pdanet: pyramid density-aware attention based network for accurate crowd counting. Neurocomputing 451, 215–230 (2021)

4. Babu, M.G., Bhalaji, C., Rajendran, S., Selvi, V.A.I.: IoT based crowd estimation and stranger recognition in closed public areas. In: 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC), pp. 763–773. IEEE, Piscataway (2021)

5. Bai, H., Mao, J., Chan, S.H.G.: A survey on deep learning-based single image crowd counting: network design, loss function and supervisory signal. Neurocomputing 508, 1–18 (2022)

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