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Robust Counting in Overcrowded Scenes Using Batch-Free Normalized Deep ConvNet

Sana Zahir1, Rafi Ullah Khan1, Mohib Ullah1, Muhammad Ishaq1, Naqqash Dilshad2, Amin Ullah3,*, Mi Young Lee4,*

1 Institute of Computer Sciences and Information Technology (ICS/IT), The University of Agriculture, Peshawar, 25130, Khyber Pakhtunkhwa, Pakistan
2 Department of Convergence Engineering for Intelligent Drone, Sejong University, Seoul, 05006, Korea
3 Collaborative Robotics and Intelligent Systems (CoRIS) Institute, Oregon State University, OR, USA
4 Department of Software, Sejong University, Seoul, 05006, Korea

* Corresponding Authors: Amin Ullah. Email: email; Mi Young Lee. Email: email

Computer Systems Science and Engineering 2023, 46(3), 2741-2754. https://doi.org/10.32604/csse.2023.037706

Abstract

The analysis of overcrowded areas is essential for flow monitoring, assembly control, and security. Crowd counting’s primary goal is to calculate the population in a given region, which requires real-time analysis of congested scenes for prompt reactionary actions. The crowd is always unexpected, and the benchmarked available datasets have a lot of variation, which limits the trained models’ performance on unseen test data. In this paper, we proposed an end-to-end deep neural network that takes an input image and generates a density map of a crowd scene. The proposed model consists of encoder and decoder networks comprising batch-free normalization layers known as evolving normalization (EvoNorm). This allows our network to be generalized for unseen data because EvoNorm is not using statistics from the training samples. The decoder network uses dilated 2D convolutional layers to provide large receptive fields and fewer parameters, which enables real-time processing and solves the density drift problem due to its large receptive field. Five benchmark datasets are used in this study to assess the proposed model, resulting in the conclusion that it outperforms conventional models.

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Cite This Article

APA Style
Zahir, S., Khan, R.U., Ullah, M., Ishaq, M., Dilshad, N. et al. (2023). Robust counting in overcrowded scenes using batch-free normalized deep convnet. Computer Systems Science and Engineering, 46(3), 2741-2754. https://doi.org/10.32604/csse.2023.037706
Vancouver Style
Zahir S, Khan RU, Ullah M, Ishaq M, Dilshad N, Ullah A, et al. Robust counting in overcrowded scenes using batch-free normalized deep convnet. Comput Syst Sci Eng. 2023;46(3):2741-2754 https://doi.org/10.32604/csse.2023.037706
IEEE Style
S. Zahir et al., "Robust Counting in Overcrowded Scenes Using Batch-Free Normalized Deep ConvNet," Comput. Syst. Sci. Eng., vol. 46, no. 3, pp. 2741-2754. 2023. https://doi.org/10.32604/csse.2023.037706



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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