Pix2Pix-Hybrid: Structure-Guided Conditional Synthesis of Hajj Crowd Images with Multi-Channel Conditioning and Weak Attribute Supervision
arXiv:2606.14297v1 Announce Type: cross Abstract: Developing accurate crowd-counting models for Hajj pilgrimage scenes remains challenging because domain-specific annotated images are scarce and data collection during large gatherings raises privacy concerns. To address these limitations, this paper proposes Pix2Pix-Hybrid (P2P-H), a hybrid conditional GAN for structure-guided Hajj crowd-image synthesis and data augmentation. P2P-H builds on Pix2Pix and employs a U-Net generator conditioned on eight input channels that jointly encode structural cues (edges and grayscale) and contextual attribu
The increasing demand for robust AI models for crowd management and surveillance, coupled with privacy concerns and data scarcity, drives innovation in synthetic data generation.
Sophisticated synthetic data generation techniques reduce reliance on sensitive real-world images and enable the development of more accurate and ethical AI systems, particularly in sensitive domains.
The ability to generate high-fidelity, structure-guided crowd images shifts the paradigm for training and validating crowd-counting AI models, mitigating data collection challenges and privacy risks.
- · AI developers
- · Crowd management solutions
- · Smart city initiatives
- · Traditional data collection methods
- · Regions lacking diverse real-world datasets
Improved accuracy and ethical compliance of AI models used in public safety and large event management.
Accelerated development and deployment of computer vision applications in security-sensitive or data-scarce environments globally.
Potential for new industries specializing in generating synthetic data for various niche applications, reducing the cost and time of AI development.
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