BBA-net: A bi-branch attention network for crowd counting

TL;DR

BBA-net introduces a dual-branch attention network that separately estimates density and location in crowd counting, improving accuracy and interpretability over traditional CNN-based methods.

Contribution

The paper presents a novel bi-branch architecture with attention mechanisms and a new density map generation method for enhanced crowd counting performance.

Findings

Achieves lower counting error than state-of-the-art methods

Effectively reduces false responses in density estimation

Enhances feature expression by integrating head and body information

Abstract

In the field of crowd counting, the current mainstream CNN-based regression methods simply extract the density information of pedestrians without finding the position of each person. This makes the output of the network often found to contain incorrect responses, which may erroneously estimate the total number and not conducive to the interpretation of the algorithm. To this end, we propose a Bi-Branch Attention Network (BBA-NET) for crowd counting, which has three innovation points. i) A two-branch architecture is used to estimate the density information and location information separately. ii) Attention mechanism is used to facilitate feature extraction, which can reduce false responses. iii) A new density map generation method combining geometric adaptation and Voronoi split is introduced. Our method can integrate the pedestrian's head and body information to enhance the feature expression ability of the density map. Extensive experiments performed on two public datasets show that our method achieves a lower crowd counting error compared to other state-of-the-art methods.