Qd2crowdnet
| dc.contributor.author | UZAIR IFTIKHAR | |
| dc.date.accessioned | 2025-12-20T11:13:52Z | |
| dc.date.available | 2025-12-20T11:13:52Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | Crowd counting and density estimation have gained significant attention in recent years due to their critical role in applications such as public safety monitoring, intelligent transportation systems, urban planning, and event management. Despite considerable progress made through methods based on convolutional neural networks (CNN), accurately estimating crowd densities remains a challenging task, especially in the presence of occlusions, complex backgrounds, perspective distortions, and varying crowd scales. While many existing models achieve high accuracy on benchmark datasets, they often struggle in complex, high-density scenarios due to limitations in architectural design. Moreover, some of these models are computationally heavy, reducing their suitability for real-time or edge deployments. To address these challenges, this thesis proposes QD2CrowdNet (Quality-Enhanced Depthwise-Dilated Crowd Network), an efficient and accurate deep learning architecture for crowd density estimation. The model leverages depthwise separable convolutions for efficiency, a multiscale dilated backend for context-aware feature extraction, and a refinement module for generating high-fidelity density maps. Extensive experiments were conducted on multiple benchmark datasets, including NWPU-Crowd, ShanghaiTech Part A and B, UCF-QNRF, and JHU-CROWD++. Results demonstrate that QD2CrowdNet consistently outperforms several state-ofthe-art methods in terms of mean absolute error (MAE) and mean squared error (MSE), while maintaining a lightweight structure suitable for real-time applications. QD2CrowdNet achieves the following MAE/MSE scores: 72.68/305.03 on NWPU-Crowd, 54.64/88.1 and 6.2/9.7 on ShanghaiTech Part A and B respectively, 76.0/120.13 on UCF-QNRF, and 52.9/210.32 on JHU-CROWD++. The findings of this study highlight the potential of efficient crowd counting architectures for realworld deployment in complex and dynamic environments. | |
| dc.identifier.uri | https://escholar.umt.edu.pk/handle/123456789/17357 | |
| dc.language.iso | en | |
| dc.publisher | UMT Lahore | |
| dc.title | Qd2crowdnet | |
| dc.title.alternative | a quality enhanced depthwise-dilated architecture for efficient and high-fidelity crowd density estimation | |
| dc.type | Thesis |