Adaptive Scenario Discovery for Crowd Counting

TL;DR

This paper introduces an adaptive framework for crowd counting that dynamically models diverse scenarios and scales, significantly improving robustness and accuracy in variable crowd images.

Contribution

The proposed method employs a multi-pathway structure with adaptive recalibration to better handle diverse crowd scenarios and densities.

Findings

Achieves state-of-the-art results on benchmark datasets.

Effectively models highly variable crowd images.

Demonstrates robustness to different scenarios and scales.

Abstract

Crowd counting, i.e., estimation number of the pedestrian in crowd images, is emerging as an important research problem with the public security applications. A key component for the crowd counting systems is the construction of counting models which are robust to various scenarios under facts such as camera perspective and physical barriers. In this paper, we present an adaptive scenario discovery framework for crowd counting. The system is structured with two parallel pathways that are trained with different sizes of the receptive field to represent different scales and crowd densities. After ensuring that these components are present in the proper geometric configuration, a third branch is designed to adaptively recalibrate the pathway-wise responses by discovering and modeling the dynamic scenarios implicitly. Our system is able to represent highly variable crowd images and achieves state-of-the-art results in two challenging benchmarks.