Consistent-point: consistent pseudo-points for semi-supervised crowd counting and localization

Abstract

Crowd counting and localization are critical for applications such as public security and traffic management. While existing methods have achieved impressive results, they rely heavily on extensive manual annotations. This paper proposes a novel point-localization-based semi-supervised crowd counting and localization method termed Consistent-Point. We identify and address two key inconsistencies of pseudo-points that have not been adequately explored. To enhance their position consistency, we aggregate the positions of neighboring auxiliary proposal-points, while an instance-wise uncertainty calibration is proposed to alleviate the class consistency of pseudo-points. By generating higher-quality pseudo-points with enhanced consistency, Consistent-Point provides more stable and effective supervision during training, yielding superior crowd counting and localization performance. Extensive experiments across five widely used datasets and three different labeled ratio settings demonstrate that our method achieves state-of-the-art performance in crowd localization while also attaining impressive crowd counting results.

Graphical abstract

Keywords

crowd counting / object counting / crowd localization / semi-supervised learning

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