Attentive multi-stage convolutional neural network for crowd counting

Abstract Crowd counting is an important problem in computer vision, whose application can be found in a wide range of tasks. Although this problem has been well studied, how to effectively deal with scale variations and perspective distortions is still a challenge. High-quality crowd density map depends heavily on how well these problems are solved. In this paper, we propose a novel network architecture called Attentive Multi-stage CNN for Crowd Counting (AMCNN). The AMCNN contains two subnetworks, i.e., hierarchical density estimator(HDE) and auxiliary count classifier (AUCC). The HDE adopts a hierarchical strategy to mine semantic features in a coarse-to-fine manner to tackle the problem of scale changes and perspective distortions. And the obtained composite features are used to generate the final density map. In addition, to further improve the density map quality, a soft attention mechanism is integrated into the AMCNN to distinct the foreground and the background. Furthermore, the AUCC is employed to achieve the count classification task, which is complementary to the task of density estimation. We evaluate our model on three public datasets: ShanghaiTech, UCF_CC_50 and Mall. Extensive experiments demonstrate that our counting model is on par with some state-of-the-art methods. Source code will be released at: https://github.com/wxq-ahu/crowd-count-amcnn .