Boosting Detection in Crowd Analysis via Underutilized Output Features
Shaokai Wu Fengyu Yang
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.