Crowd counting with segmentation attention convolutional neural network

TL;DR

This paper introduces SegCrowdNet, a CNN that uses segmentation and attention mechanisms to improve crowd counting accuracy by focusing on human head regions and handling scale variations.

Contribution

The novel SegCrowdNet architecture integrates segmentation, attention, and multi-scale features for enhanced crowd counting performance.

Findings

Achieves superior accuracy on four challenging datasets.

Effectively highlights human head regions in complex scenes.

Handles scale variations through multi-scale feature learning.

Abstract

Deep learning occupies an undisputed dominance in crowd counting. In this paper, we propose a novel convolutional neural network (CNN) architecture called SegCrowdNet. Despite the complex background in crowd scenes, the proposeSegCrowdNet still adaptively highlights the human head region and suppresses the non-head region by segmentation. With the guidance of an attention mechanism, the proposed SegCrowdNet pays more attention to the human head region and automatically encodes the highly refined density map. The crowd count can be obtained by integrating the density map. To adapt the variation of crowd counts, SegCrowdNet intelligently classifies the crowd count of each image into several groups. In addition, the multi-scale features are learned and extracted in the proposed SegCrowdNet to overcome the scale variations of the crowd. To verify the effectiveness of our proposed method, extensive experiments are conducted on four challenging datasets. The results demonstrate that our proposed SegCrowdNet achieves excellent performance compared with the state-of-the-art methods.