Detection-based methods have been viewed unfavorably in crowd analysis due to their poor performance in dense crowds. However, we argue that the potential of these methods has been underestimated, as they offer crucial information for crowd analysis that is often ignored. Specifically, the area size and confidence score of output proposals and bounding boxes provide insight into the scale and density of the crowd. To leverage these underutilized features, we propose Crowd Hat, a plug-and-play module that can be easily integrated with existing detection models. This module uses a mixed 2D-1D compression technique to refine the output features and obtain the spatial and numerical distribution of crowd-specific information. Based on these features, we further propose region-adaptive NMS thresholds and a decouple-then-align paradigm that address the major limitations of detection-based methods. Our extensive evaluations on various crowd analysis tasks, including crowd counting, localization, and detection, demonstrate the effectiveness of utilizing output features and the potential of detection-based methods in crowd analysis.

基于检测方法的人群分析研究，通过利用输出特征和检测方法的潜力展示了在不同的人群分析任务中，如人群计数、定位和检测中，Crowd Hat模块通过混合的2D-1D压缩技术和区域自适应的NMS阈值以及解耦-对齐的范式来提供评估数据和实际效能。

通过未充分利用的输出特征提升众包分析中的检测