DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation

TL;DR

DecideNet is an adaptive crowd counting framework that combines detection and density estimation with an attention mechanism to handle varying crowd densities effectively.

Contribution

It introduces an end-to-end network that adaptively chooses between detection and regression-based counting using an attention module for different density regions.

Findings

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

Effectively handles scenes with varying crowd densities.

Outperforms existing methods in accuracy and robustness.

Abstract

In real-world crowd counting applications, the crowd densities vary greatly in spatial and temporal domains. A detection based counting method will estimate crowds accurately in low density scenes, while its reliability in congested areas is downgraded. A regression based approach, on the other hand, captures the general density information in crowded regions. Without knowing the location of each person, it tends to overestimate the count in low density areas. Thus, exclusively using either one of them is not sufficient to handle all kinds of scenes with varying densities. To address this issue, a novel end-to-end crowd counting framework, named DecideNet (DEteCtIon and Density Estimation Network) is proposed. It can adaptively decide the appropriate counting mode for different locations on the image based on its real density conditions. DecideNet starts with estimating the crowd density by generating detection and regression based density maps separately. To capture inevitable variation in densities, it incorporates an attention module, meant to adaptively assess the reliability of the two types of estimations. The final crowd counts are obtained with the guidance of the attention module to adopt suitable estimations from the two kinds of density maps. Experimental results show that our method achieves state-of-the-art performance on three challenging crowd counting datasets.