Consistency-Aware Anchor Pyramid Network for Crowd Localization
This work improves crowd localization for applications like surveillance and public safety, but it is incremental as it builds on existing anchor-based methods.
The paper tackled crowd localization by addressing ranking inconsistency and fixed anchor resolution issues, achieving favorable performance on three datasets (ShanghaiTech A&B, JHU-CROWD++, UCF-QNRF) against state-of-the-art methods.
Crowd localization aims to predict the spatial position of humans in a crowd scenario. We observe that the performance of existing methods is challenged from two aspects: (i) ranking inconsistency between test and training phases; and (ii) fixed anchor resolution may underfit or overfit crowd densities of local regions. To address these problems, we design a supervision target reassignment strategy for training to reduce ranking inconsistency and propose an anchor pyramid scheme to adaptively determine the anchor density in each image region. Extensive experimental results on three widely adopted datasets (ShanghaiTech A\&B, JHU-CROWD++, UCF-QNRF) demonstrate the favorable performance against several state-of-the-art methods.