Boosting Detection in Crowd Analysis via Underutilized Output Features

TL;DR

This paper demonstrates that detection-based methods, when enhanced with a novel feature utilization approach, can significantly improve crowd analysis tasks such as counting and localization.

Contribution

It introduces Crowd Hat, a plug-and-play module that refines output features to boost detection performance in dense crowd analysis.

Findings

Improved crowd counting accuracy

Enhanced localization and detection performance

Effective integration with existing models

Abstract

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.