Title: Optimised attention-based CNN for crowd density estimation with wiener filtering

Authors: Jyoti Ambadas Kendule; Kailash J. Karande

Addresses: SKN Sinhagad College of Engineering, Korti, Pandharpur, Maharashtra 413304, India; Department of Electronics and Telecommunication, Fabtech Technical Campus, COE and Research, Sangola, Maharashtra 413304, India ' Department of Electronics and Telecommunication, SKN Sinhagad College of Engineering, Korti, Pandharpur, Maharashtra 413304, India; PAH Solapur University, Kegaon, Solapur, Maharashtra 413255, India

Abstract: Crowd density estimation, the process of quantifying individuals in a given space, is crucial for urban planning, public safety, and event management. Accurate estimation supports effective decision-making, resource allocation, and crowd control. Traditional methods often rely on manual counting or simple techniques that fail to address the complexities of crowded scenes. To overcome these limitations, this paper introduces a novel deep learning-based crowd density estimation (DL-CDE) framework. The framework begins by converting video footage of the crowd into individual frames, enabling detailed analysis. These frames undergo pre-processing with a root mean square assisted wiener filtering (RAWF) technique, which enhances clarity and reduces noise distortion. The processed frames are then fed into a modified attention mechanism-based convolutional neural network (MA-CNN), which performs both object detection and crowd density estimation. This approach provides more accurate and reliable crowd density predictions, improving the effectiveness of crowd management and safety measures in various settings.

Keywords: crowd density estimation; deep learning; DL; root mean square assisted wiener filtering; RAWF-based pre-processing; mechanism-based convolutional neural network; MA-CNN; attention mechanism.

DOI: 10.1504/IJAMECHS.2026.150498

International Journal of Advanced Mechatronic Systems, 2026 Vol.13 No.1, pp.16 - 32

Received: 15 Nov 2024
Accepted: 10 Jul 2025
Published online: 15 Dec 2025 *

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