One-Shot Crowd Counting With Density Guidance For Scene Adaptation
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.