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Consistency-Aware Anchor Pyramid Network for Crowd Localization

2022-12-08 04:32:01
Xinyan Liu, Guorong Li, Yuankai Qi, Zhenjun Han, Qingming Huang, Ming-Hsuan Yang, Nicu Sebe
arXiv_CV
   
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2212.04067

PDF

https://arxiv.org/pdf/2212.04067.pdf

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