arXiv:2606.18566v1 Announce Type: cross Abstract: Crowd counting is a fundamental task in computer vision. However, crowd counting in low-light environments remains largely underexplored, despite its practical importance in the real world. Existing methods mainly focus on well-lit scenes or rely on single-modality Red-Green-Blue (RGB) representations, which often become unreliable under extreme darkness and complex non-uniform illumination. To handle this problem, we construct three new low-light crowd counting benchmarks, which consist of two synthetic datasets, SHA\_Dark and SHB\_Dark, and a
The paper addresses a critical gap in computer vision as AI models mature and encounter real-world conditions beyond ideal lighting, highlighting the ongoing push for robust AI in diverse environments.
This development improves AI's ability to operate in challenging low-light conditions, crucial for applications like public safety, surveillance, and autonomous systems where visibility is often compromised.
Previous limitations of crowd counting in low-light environments are being systematically addressed through multi-modal data fusion and new benchmark datasets, shifting from RGB-only dependence.
- · AI developers in computer vision
- · Public safety and surveillance sectors
- · Developers of autonomous systems
- · Legacy single-modality vision systems
Improved accuracy and reliability of crowd counting in challenging low-light conditions.
Enhanced capabilities for nocturnal surveillance, smart city management, and accident prevention.
Broader adoption of AI vision systems in environments previously deemed unsuitable due to lighting constraints, potentially impacting urban planning and security infrastructure.
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Read at arXiv cs.AI