论文标题
一致性感知的锚定金字塔网络，用于人群本地化
Consistency-Aware Anchor Pyramid Network for Crowd Localization
论文作者
论文摘要
人群本地化旨在预测人类在人群场景中的空间位置。我们观察到，现有方法的性能是从两个方面挑战的：（i）在测试阶段和训练阶段之间排名不一致； （ii）固定锚固分辨率可能不足以使当地地区的人群密度过高或过度拟合。为了解决这些问题，我们设计了一个监督目标重新分配策略，以减少排名不一致，并提出一种锚金字塔方案，以适应每个图像区域中的锚固密度。在三个广泛采用的数据集（Shanghaitech A \＆B，Jhu-Crowd ++，UCF-QNRF）上进行的广泛实验结果表明，针对几种最新方法的性能。
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.
