Crowd counting and localization have become increasingly important in computer vision due to their wide-ranging applications. While point-based strategies have been widely used in crowd counting methods, they face a significant challenge, i.e., the lack of an effective learning strategy to guide the matching process. This deficiency leads to instability in matching point proposals to target points, adversely affecting overall performance. To address this issue, we introduce an effective approach to stabilize the proposal-target matching in point-based methods. We propose Auxiliary Point Guidance (APG) to provide clear and effective guidance for proposal selection and optimization, addressing the core issue of matching uncertainty. Additionally, we develop Implicit Feature Interpolation (IFI) to enable adaptive feature extraction in diverse crowd scenarios, further enhancing the model's robustness and accuracy. Extensive experiments demonstrate the effectiveness of our approach, showing significant improvements in crowd counting and localization performance, particularly under challenging conditions. The source codes and trained models will be made publicly available.

翻译：人群计数与定位因其广泛的应用场景在计算机视觉中日益重要。尽管点基策略已被广泛用于人群计数方法，但仍面临一个重大挑战，即缺乏有效的学习策略来指导匹配过程。这一缺陷导致候选点与目标点匹配的不稳定性，进而影响整体性能。为解决该问题，我们提出了一种有效方法以稳定点基方法中的候选-目标匹配过程。我们引入辅助点指导（APG），为候选点选择与优化提供明确有效的指导，从而解决匹配不确定性的核心问题。此外，我们开发了隐式特征插值（IFI），实现在多样化人群场景中的自适应特征提取，进一步增强模型的鲁棒性与准确性。大量实验证明了该方法的有效性，在人群计数与定位性能上取得显著提升，尤其在具有挑战性的条件下。相关源代码与训练模型将公开发布。