Open Access

ARTICLE

Multi-Scale Network with Integrated Attention Unit for Crowd Counting

by Adel Hafeezallah¹, Ahlam Al-Dhamari^2,3,*, Syed Abd Rahman Abu-Bakar²

1 Department of Electrical Engineering, Taibah University, Madinah, Saudi Arabia
2 Department of Electronics and Computer Engineering, School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor, Malaysia
3 Department of Computer Engineering, Hodeidah University, Hodeidah, Yemen

* Corresponding Author: Ahlam Al-Dhamari. Email:

Computers, Materials & Continua 2022, 73(2), 3879-3903. https://doi.org/10.32604/cmc.2022.028289

Received 07 February 2022; Accepted 28 April 2022; Issue published 16 June 2022

Abstract

Estimating the crowd count and density of highly dense scenes witnessed in Muslim gatherings at religious sites in Makkah and Madinah is critical for developing control strategies and organizing such a large gathering. Moreover, since the crowd images in this case can range from low density to high density, detection-based approaches are hard to apply for crowd counting. Recently, deep learning-based regression has become the prominent approach for crowd counting problems, where a density-map is estimated, and its integral is further computed to acquire the final count result. In this paper, we put forward a novel multi-scale network (named 2U-Net) for crowd counting in sparse and dense scenarios. The proposed framework, which employs the U-Net architecture, is straightforward to implement, computationally efficient, and has single-step training. Unpooling layers are used to retrieve the pooling layers’ erased information and learn hierarchically pixel-wise spatial representation. This helps in obtaining feature values, retaining spatial locations, and maximizing data integrity to avoid data loss. In addition, a modified attention unit is introduced and integrated into the proposed 2U-Net model to focus on specific crowd areas. The proposed model concentrates on balancing the number of model parameters, model size, computational cost, and counting accuracy compared with other works, which may involve acquiring one criterion at the expense of other constraints. Experiments on five challenging datasets for density estimation and crowd counting have shown that the proposed model is very effective and outperforms comparable mainstream models. Moreover, it counts very well in both sparse and congested crowd scenes. The 2U-Net model has the lowest MAE in both parts (Part A and Part B) of the ShanghaiTech, UCSD, and Mall benchmarks, with 63.3, 7.4, 1.5, and 1.6, respectively. Furthermore, it obtains the lowest MSE in the ShanghaiTech-Part B, UCSD, and Mall benchmarks with 12.0, 1.9, and 2.1, respectively.

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Keywords

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