English(EN) BEM: Training-Free Background Embedding Memory for False-Positive Suppression in Real-Time Fixed-Background Camera

新的BEM模块可抑制实时摄像机检测中的误报

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-05 04:00

研究人员开发了一种名为背景嵌入记忆（BEM）的新型无训练模块，旨在提高目标检测器在现实场景中的准确性。BEM通过估计背景嵌入并利用它们来惩罚虚假检测，从而在无需额外训练的情况下减少误报。该方法在LLVIP等数据集和模拟监控流上的各种检测器家族中显示出一致的改进，并保持了实时性能。 AI

影响该方法可以通过减少误报来提高AI视觉系统在监控和交通监控中的可靠性。

排序理由这是一篇详细介绍一种新方法的学术论文，用于改进目标检测。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CV TIER_1 English(EN) · Junwoo Park, Jangho Lee, Sunho Lim · 2026-05-05 04:00

BEM：用于实时固定背景摄像机误报抑制的无训练背景嵌入记忆

arXiv:2604.11714v2 Announce Type: replace Abstract: Pretrained detectors perform well on benchmarks but often suffer performance degradation in real-world deployments due to distribution gaps between training data and target environments. COCO-like benchmarks emphasize category d…