New one-shot crowd counting method adapts to unseen scenes

By PulseAugur Editorial · [1 sources] · 2026-06-02 04:00

Researchers have developed a novel one-shot crowd counting method that adapts to new surveillance scenes by leveraging local and global density characteristics. The approach uses multiple local density learners to capture varying density distributions and global density features to guide the model. Experiments on three datasets demonstrate that this method outperforms existing state-of-the-art techniques in few-shot crowd counting scenarios. AI

IMPACT This method could improve the accuracy of surveillance systems in diverse and previously unencountered environments.

RANK_REASON The cluster contains an academic paper detailing a new method for crowd counting. [lever_c_demoted from research: ic=1 ai=1.0]

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Chen Jiwei

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arXiv cs.CV TIER_1 English(EN) · Jiwei Chen, Qi Wang, Junyu Gao, Jing Zhang, Dingyi Li, Jing-Jia Luo · 2026-06-02 04:00

One-Shot Crowd Counting With Density Guidance For Scene Adaptation

arXiv:2602.07955v2 Announce Type: replace Abstract: Crowd scenes captured by cameras at different locations vary greatly, and existing crowd models have limited generalization for unseen surveillance scenes. To improve the generalization of the model, we regard different surveill…

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One-Shot Crowd Counting With Density Guidance For Scene Adaptation

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