English(EN) DensePed-Lite: Quality-Aware Adaptive Detection for Dense Pedestrians under Occlusion

DensePed-Lite 改进了城市遮挡场景下的行人检测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-01 04:00

研究人员推出了一种新颖的框架 DensePed-Lite，旨在改进密集、遮挡的城市环境中的行人检测。该系统根据观察到的特征质量自适应其检测行为，尤其是在存在遮挡时。该方法通过三个关键机制实现：不可靠质量估计 (UQE)、多点空间覆盖 (MPSC) 和连贯时间检测建模 (CTDM)，它们协同工作以提高稳定性和准确性，而不会显著增加复杂性。在 CityPersons 和 CrowdHuman 等基准数据集上的实验表明，DensePed-Lite 与现有的轻量级方法相比，在准确性和效率方面提供了更优的平衡，使其适用于实时应用。 AI

影响这种新的检测框架可以提高自动驾驶和公共安全系统在复杂城市环境中的安全性和效率。

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · ZiAn Wang, MingZhe Liu, Chaoyi Guo, ChangChun Li, Fangming Gu · 2026-10-01 04:00

DensePed-Lite：遮挡下具有质量感知的自适应密集行人检测

arXiv:2609.39467v1 Announce Type: new Abstract: Pedestrian detection plays a crucial role in computer vision with applications in autonomous driving, surveillance, and public safety. However, real-world dense scenes bring severe challenges, including heavy occlusion, drastic scal…

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DensePed-Lite：遮挡下具有质量感知的自适应密集行人检测

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