研究人员开发了一种用于低光照条件下人群计数的新方法，这是一个探索不足的领域。该方法引入了三个新数据集：SHA_Dark、SHB_Dark 和 LC-Crowd。它利用基于 Retinex 的模型，结合深度和边缘线索来增强反射率表示，并提出了一个全模态超图融合模块来整合 RGB 外观、深度几何和边缘结构。此外，还引入了一个可变形矩形稀疏注意力模块来优化密集预测的计算，从而形成一个统一的低光照计数网络（LCNet），据报道其性能优于现有的最先进方法。 AI
影响 这项研究可以改善在具有挑战性的低光照环境中的监控和分析。
排序理由 该集群包含一篇学术论文，详细介绍了用于计算机视觉任务的新方法和数据集。
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
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-li…
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…
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…