研究人员推出了HAJJv2-CrowdCount，这是一个专为朝觐视频素材设计的、用于密集人群计数的新的基准数据集。该数据集解决了陡峭的摄像角度、大范围遮挡和高人群密度等独特挑战。对三种零样本计数方法——YOLO-World、APGCC和SAM3Count——的基准测试显示，虽然SAM3Count总体表现最佳，但在对朝觐人群管理至关重要的最密集、最遮挡的场景中，基于点的计数器APGCC被证明更可靠。 AI
影响 该基准测试可能导致高密度环境中更鲁棒的人群管理系统。
排序理由 该集群包含一篇介绍特定计算机视觉任务新颖数据集和基准的学术论文。
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arXiv:2607.07322v1 Announce Type: cross Abstract: Automated crowd counting in Hajj video is difficult not because current models lack capacity, but because the footage violates the assumptions those models were built on: cameras observe the crowd from steep, near-vertical angles,…
Automated crowd counting in Hajj video is difficult not because current models lack capacity, but because the footage violates the assumptions those models were built on: cameras observe the crowd from steep, near-vertical angles, individuals occlude one another extensively, and …
Automated crowd counting in Hajj video is difficult not because current models lack capacity, but because the footage violates the assumptions those models were built on: cameras observe the crowd from steep, near-vertical angles, individuals occlude one another extensively, and …