Consistent-Point: Consistent Pseudo-Points for Semi-Supervised Crowd Counting and Localization
This work addresses the need for more efficient crowd analysis in applications like public security and traffic management by reducing reliance on extensive annotations, representing an incremental improvement over existing methods.
The paper tackles the problem of inconsistent pseudo-points in semi-supervised crowd counting and localization by aggregating neighboring proposal-points and using instance-wise uncertainty calibration, achieving state-of-the-art performance in localization and impressive counting results across five datasets and three labeled ratio settings.
Crowd counting and localization are important in applications such as public security and traffic management. Existing methods have achieved impressive results thanks to extensive laborious annotations. This paper propose a novel point-localization-based semi-supervised crowd counting and localization method termed Consistent-Point. We identify and address two inconsistencies of pseudo-points, which have not been adequately explored. To enhance their position consistency, we aggregate the positions of neighboring auxiliary proposal-points. Additionally, an instance-wise uncertainty calibration is proposed to improve the class consistency of pseudo-points. By generating more consistent pseudo-points, Consistent-Point provides more stable supervision to the training process, yielding improved results. 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. The code will be available.