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Original Article
Safe Crowd: Real-Time Crowd Monitoring System for Safety and Occupancy Alerts
P V Prasanna Kumari1
K. Aravind Kumar2
M. Hemanth Kumar3
B. Gowripriya4
1 Assistant Professor, Department of CSE (Data Science), RGMCET, Nandyal, Andhra Pradesh, India. 2 3 4Department of Computer Science and Engineering (Data Science), Rajeev Gandhi Memorial College of Engineering and Technology, Nandyal, Andhra Pradesh, India.
Published Online: January-April 2026
Pages: 432-437
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260501048References
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Analysis of Crowds, Springer, 2013, pp. 347–382.
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CVPR, 2016, pp. 589–597.
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Information Sciences, vol. 65, no. 6, pp. 1–14, 2022.
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3367–3379, 2023.
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Pattern Anal. Mach. Intell., vol. 44, no. 5, pp. 2594–2609, 2022.
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counting. J. Comput. Vis., vol. 130, no. 2, pp. 405–434, 2022
windows, in Proc. ICCV, 2021, pp. 10012–10022.
2. C. Loy, K. Chen, S. Gong, and T. Xiang, Crowd counting and profiling: Methodology and evaluation, in Modeling, Simulation and Visual
Analysis of Crowds, Springer, 2013, pp. 347–382.
3. Y. Zhang, D. Zhou, S. Chen, S. Gao and Y. Ma, Single-image crowd counting using multi-column convolutional neural network, in Proc.
CVPR, 2016, pp. 589–597.
4. Y. Li, X. Zhang, and D. Chen, CSRNet: Dilated convolutional neural networks to comprehend the congested scenes, in Proc. CVPR, 2018,
pp. 1091–1100.
5. W. Liu, M. Salzmann and P. Fua, Context-aware crowd counting in Proc. CVPR, 2019, pp. 5099–5108.
6. D. Liang, X. Chen, W. Xu, Y. Zhou, and X. Bai, TransCrowd: Weakly-supervised crowd counting with transformers, Science China
Information Sciences, vol. 65, no. 6, pp. 1–14, 2022.
7. Dong, F. Zeng, T. Wang, D. Zhang and Y. Shi, CCST: Crowd counting with Swin Transformer, The Visual Computer, vol. 39, no. 8, pp.
3367–3379, 2023.
8. V. A. Sindagi, R. Yasarla, and V. M. Patel, "JHU-Crowd++: Large-scale crowd counting dataset and a benchmark method," IEEE Trans.
Pattern Anal. Mach. Intell., vol. 44, no. 5, pp. 2594–2609, 2022.
9. R. Wightman, "PyTorch Image Models (TIMM)," GitHub repository, 2019. [Online]. Available: https://github.com/rwightman/pytorch-
image-models
10. X. Cao, Z. Wang, Y. Zhao and F. Su, Scale aggregation network: accurate and efficient crowd counting, in Proc. ECCV, 2018, pp. 757–773.
11. H. Lee, K. Lee, J. Kang, and K. Sohn, "Training a regression-based model to count cars in the crowd based on ranked image pairs and triplets,
IEEE Access, vol. 12, pp. 12818–12826, 2024. W. Xu, J. Ma, K. Zhao, X. Chang, and A. Hauptmann, Autoscale: Learning to scale to crowd
counting. J. Comput. Vis., vol. 130, no. 2, pp. 405–434, 2022
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